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Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

Most breast cancer lesions absorb higher levels of near-infrared (NIR) radiation compared to healthy breast tissue due to its increased vascularity. Oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) primarily found in cancerous vascular lesions, absorbs higher levels of radiation in the 650 nm to 850 nm wavelength range than the surrounding fatty tissue and water in the human breast. NIR diffuse optical spectroscopy (DOS) provides real-time functional and compositional information based on the optical properties of biological tissues, which cannot be accomplished by other portable breast imaging modalities. Here we present the first set of clinical trials using a non-invasive, hand-held diffuse optical breast scanner (DOB-Scan probe3) to capture in vivo cross-sectional images of the breast. The scanner uses four NIR illuminating sources with different wavelengths, 690 nm, 750 nm, 800 nm, and 850 nm, to determine the concentrations of the four main constituents of breast tissue, oxy-hemoglobin (HbO2), deoxy-hemoglobin (Hb), water (H2O), and fat. In this paper, we briefly explain the hardware design and image reconstruction algorithm of the DOB-Scan probe, the data collection process, and the imaging results of four different participants, selected from twenty, all who are diagnosed with breast cancer. For each patient, images were scanned from two locations, the first over the cancerous lesion and the second over the same region on the contralateral healthy breast, as a means of establishing controls for comparison. During each scan, four cross-sectional images of the breast, corresponding to four different NIR wavelengths, are reconstructed and displayed on a user interface for reference. Clinical results confirm that the absorption coefficients of cancerous lesions are significantly higher than the normal surrounding tissue. We propose to deploy the probe to effectively identify cancerous breast tissue at an early stage in a primary care setting, which could increase the efficiency of screening programs.

Recently, micro electro-mechanical systems (MEMS) inertial sensors have found their way in various applications. These sensors are fairly low cost and easily available but their measurements are noisy and imprecise, which poses the necessity of calibration. In this paper, we present an approach to calibrate an inertial measurement unit (IMU) comprised of a low-cost tri-axial MEMS accelerometer and a gyroscope. As opposed to existing methods, our method is truly infield as it requires no external equipment and utilizes gravity signal as a stable reference. It only requires the sensor to be placed in approximate orientations, along with the application of simple rotations. This also offers easier and quicker calibration comparatively. We analyzed the method by performing experiments on two different IMUs: an in-house built IMU and a commercially calibrated IMU. We also calibrated the in-house built IMU using an aviation grade rate table for comparison. The results validate the calibration method as a useful low-cost IMU calibration scheme.

Xerostomia, or dry mouth syndrome, is one of the radiation therapy (RT)-induced side effects experienced by head and neck cancer patients. Measuring the severity of xerostomia can be performed by several techniques such as measuring the differential weight of dental rolls placed in the mouth for a fixed time period, measuring the electromyographic activity of musculus digastricus, using parotid cup, and electrophysiological measurements by electrical impedance. The present study focuses on detecting the change in bioimpedance around the oral cavity when salivation is induced using lime juice on healthy human subjects. In this study, impedance changes between stimulated and resting states of the salivary glands for 31 healthy volunteers were measured and analyzed. Pre- and post-stimulation impedance measurement was performed on healthy volunteer subjects using impedance spectroscopy over 50 frequencies ranging from 10 kHz to 1 MHz. The electrical properties of salivary glands were extracted by fitting the measured admittance data to the well-known Cole model. Comparison of measured electrical properties of gland at the resting and stimulated states reveals the changes in the intra- and extra-cellular spaces of salivary gland as a result of saliva secretion. Statistical analysis of the spectroscopy data collected shows that there is a significant difference in electrical properties of tissue between resting and stimulated states of the salivary activity. These electrical impedance spectroscopy measurements will be used to benchmark changes of impedance due to salivary gland stimulation.

Formation of membrane pinholes is a common defect in fuel cells, inflicting more cost and making less durable cells. This work focuses on mitigating this issue, and offers a continuous online treatment instead of attempting to dynamically model the hydrogen transfer leak rate. This is achieved by controlling the differential pressure between the anode and cathode compartments at the inlet side of the fuel cell stack, known as the fuel overpressure. The model predictive control approach is used to attain the objectives in a Ballard 9-cell Mk1100 polymer electrolyte membrane fuel cell (PEMFC) with inclusion of hydrogen transfer leak. Furthermore, the pneumatic modeling technique is used to model the entire anode side of a fuel cell station. The hydrogen transfer leak is embedded in the model in a novel way, and is considered as a disturbance during the controller design. Experimental results for different sizes of hydrogen transfer leaks are provided to show the benefits of fuel overpressure control system in alleviating the effects of membrane pinholes, which in turn increases membrane longevity, and reduces hydrogen emissions in the eventual presence of transfer leaks. Moreover, the model predictive controller provides an optimal control input while satisfying the problem constraints.

This paper focuses on the design of a tuning fork microresonator that utilizes its inherent inertial nonlinearities for its operation. Two orthogonal in-plane modes of the microresonator are carefully designed so that the natural frequency ratio between the higher and the lower frequency modes is close to two. When we electrostatically excite the higher frequency mode with the excitation voltage higher than some threshold, the lower frequency mode gets autoparametrically excited due to a nonlinear resonance phenomenon known as two-to-one (2:1) internal resonance. We employed the MEMS Integrated Design for Inertial Sensors (MIDIS), a high vacuum bulk-micromachining process, by Teledyne Dalsa to fabricate the proposed microresonator. We then experimentally demonstrate the 2:1 internal resonance phenomenon occurring in the resonator and highlight its applicability in developing the resonating MEMS based sensors.

To mitigate the injurious effect of the rotational acceleration of the brain, a modular Impact Diverting Mechanism (IDM) has been developed. The IDM can replace stickers (decals) that normally attach to the exterior of a football helmet. The IDM decals reduce friction and catch points between the covered area with the IDM on the outer shell of the helmet and the impacting surface, thereby decreasing rotational acceleration acting on the player’s head. A Riddell Speed helmet’s exterior was prepared with the IDM and outfitted to a headform equipped with linear accelerometers and gyroscopes. The helmets were tested at an impact velocity of 5.5 m/s at 15°, 30°, and 45° to the vertical: on the front, side, and back of the helmet. Results of 135 impact tests in the lab show that the IDM decal, when compared to helmets without it, reduced the rotational acceleration, rotational velocity, SI, HIC, and RIC ranging from 22% to 77%, 20% to 74%, 13% to 68%, 7% to 68%, 31% to 94%, respectively. Protection against rotational acceleration from oblique impacts is not prioritized in modern football helmets, as evident by current standard helmet testing protocols. This study demonstrates that the inclusion of the IDM decals in football helmets can help reduce the effects of rotational acceleration of the head during oblique impacts.

Diffuse optical spectroscopy is a relatively new, noninvasive and nonionizing technique for breast cancer diagnosis. In the present study, we have introduced a novel handheld diffuse optical breast scan (DOB-Scan) probe to measure optical properties of the breast in vivo and create functional and compositional images of the tissue. In addition, the probe gives more information about breast tissue’s constituents, which helps distinguish a healthy and cancerous tissue. Two symmetrical light sources, each including four different wavelengths, are used to illuminate the breast tissue. A high-resolution linear array detector measures the intensity of the back-scattered photons at different radial destinations from the illumination sources on the surface of the breast tissue, and a unique image reconstruction algorithm is used to create four cross-sectional images for four different wavelengths. Different from fiber optic-based illumination techniques, the proposed method in this paper integrates multi-wavelength light-emitting diodes to act as pencil beam sources into a scattering medium like breast tissue. This unique design and its compact structure reduce the complexity, size and cost of a potential probe. Although the introduced technique miniaturizes the probe, this study points to the reliability of this technique in the phantom study and clinical breast imaging. We have received ethical approval to test the DOB-Scan probe on patients and we are currently testing the DOB-Scan probe on subjects who are diagnosed with breast cancer.

This paper proposes a mechatronic suspension system with capabilities of energy regeneration and semi-active control [i.e., skyhook (SK)], incorporated into the mechanism. The system overcomes the trade-off between energy consumption and ride handling/comfort in a semi-active/active suspension system. Central to the concept is the development of a switched-mode rectifier (SMR) capable of providing either a positive or negative damping ratio by controlling the electric current to alternate between regenerative and motoring modes. Using the implemented circuit, a regenerative SK control strategy is utilized to provide a continuous variable damping force that significantly improves the vibration isolation. Simulation results are presented to demonstrate power flow in both regenerating and motoring modes by utilizing a current controller. Furthermore, experimental results are presented highlighting performance of the regenerative suspension prototype in terms of power regeneration, sprung mass absolute acceleration, and relative displacement. In addition, both theoretical and experimental power conversion efficiencies in various segments of the mechatronic suspension are analyzed and reported.

This paper proposes an algorithm to control the suspension dynamics with an energy regeneration capability and high conversion efficiency. Central to the concept is the development of a switched-mode rectifier (SMR) that is capable of providing either an equivalent positive or negative damping and alternating between regenerative and motoring modes. Simulation results are presented to demonstrate power flow in regeneration and motoring modes and verify the equivalent damping characteristic and vibration energy harvesting. The studies are conducted on a small-scale proof-of-concept electromagnetic suspension system excited by emulated International Standard Organization (ISO) 8608 road profiles. An autonomous start/stop algorithm is proposed such that autonomously turns on or off the converter. This mechanism mitigates quiescent power consumption of auxiliary integrated circuits (ICs) such as gate drivers and current transducers, therefore increasing the conversion efficiency of the power electronics throughout an ISO standardized variable-speed driving cycle. The autonomous start/stop algorithm is implemented experimentally to demonstrate its feasibility under typical standardized driving cycles.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through in vivo tissue to assess its optical properties and identify abnormalities such as cancerous lesions. While this relatively new optical imaging modality is cost-effective and non-invasive, its inverse problem (i.e., recovering an image from raw signal measurements) is ill-posed, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. Solving the inverse problem becomes even more challenging in the case of limited-angle data acquisition given the restricted number of sources and sensors, the sparsity of the recovered information, and the presence of noise, representative of real world acquisition environments. Traditional optimization-based reconstruction methods are computationally intensive and thus too slow for real-time imaging applications. We propose a novel image reconstruction method for breast cancer DOT imaging. Our method is highlighted by two components: (i) a deep learning network with a novel hybrid loss, and (ii) a distribution transfer learning module. Our model is designed to focus on lesion specific information and small reconstruction details to reduce reconstruction loss and lesion localization errors. The transfer learning module alleviates the need for real training data by taking advantage of cross-domain learning. Both quantitative and qualitative results demonstrate that the proposed method’s accuracy surpasses existing methods’ in detecting tissue abnormalities.

In this paper a vehicle suspension system with energy harvesting capability is developed, and an analytical methodology for the optimal design of the system is proposed. The optimization technique provides design guidelines for determining the stiffness and damping coefficients aimed at the optimal performance in terms of ride comfort and energy regeneration. The corresponding performance metrics are selected as root-mean-square (RMS) of sprung mass acceleration and expectation of generated power. The actual road roughness is considered as the stochastic excitation defined by ISO 8608:1995 standard road profiles and used in deriving the optimization method. An electronic circuit is proposed to provide variable damping in the real-time based on the optimization rule. A test-bed is utilized and the experiments under different driving conditions are conducted to verify the effectiveness of the proposed method. The test results suggest that the analytical approach is credible in determining the optimality of system performance.

In this study, a new switched-mode bridgeless converter operating in the discontinuous conduction mode is presented along with an input resistance control scheme. Through feedback control, the converter-battery circuitry synthesises variable line resistor with regenerative capability is intended for large-scale electromagnetic-based energy harvesting systems. For instance, the circuit can operate as a vehicular regenerative damper through converting vibration energy into battery charge. Analysis of the power converter in various switching modes is presented along with implementation of a control method for synthesising a desired input resistance. Experimental and simulation results are presented that highlight operations and power efficiencies of the proposed power converter along with its control scheme.

Diffuse optical tomography (DOT) is a relatively new imaging modality that has demonstrated its clinical potential of probing tumors in a non-invasive and affordable way. Image reconstruction is an ill-posed challenging task because knowledge of the exact analytic inverse transform does not exist a priori, especially in the presence of sensor non-idealities and noise. Standard reconstruction approaches involve approximating the inverse function and often require expert parameters tuning to optimize reconstruction performance. In this work, we evaluate the use of a deep learning model to reconstruct images directly from their corresponding DOT projection data. The inverse problem is solved by training the model via training pairs created using physics-based simulation. Both quantitative and qualitative results indicate the superiority of the proposed network compared to an analytic technique.

We are reporting the design and experimental investigation on a microresonator simultaneously exhibiting nonlinear modal interactions due to 1/2 subharmonic resonance and 2:1 internal resonance. This has been made possible through careful design of the structure with attention to the limits of microfabricated devices. Finite element models of the structure were built and used to layout the device with two desired vibrational modes with a ∼2:1 frequency ratio. The microresonator was fabricated in MEMS Integrated Design for Inertial Sensors (MIDIS) from Teledyne DALSA Semiconductor Inc. (TDSI). Extensive experiments were conducted to verify the intended nonlinear responses. The device design methodologies can be employed to fabricate various sensors, including nonlinear gyroscopes incorporating 2:1 internal resonance.

Diffuse optical spectroscopy (DOS) and diffuse optical tomography (DOT) are non-invasive breast cancer assessment modalities which employ near-infrared (NIR) light to measure optical properties of biological tissue. These properties cannot be measured by other methods including X-ray mammography, ultrasound (US) and magnetic resonance imaging (MRI) which are main breast cancer diagnosis tools. The objective of this paper is to test and validate a recently designed and developed hand-held continuous-wave radio-frequency modulated diffuse optical spectroscopy probe in a clinical trial performed on patients who diagnosed to have breast cancer. The probe has an encapsulated light emitting diode (eLED) including four wavelengths (690 nm, 750 nm, 800 nm and 850 nm) and two photodiodes located in reflectance geometry. The direct approach method has been used to extract concentration of two main chronophers in the breast tissue including deoxy-hemoglobin (Hb) and oxy-hemoglobin (HbO2). The results of the clinical trial, which included fourteen patients, show that the RF-DOS probe can correctly classify the cancerous lesion from healthy tissue in the breast. The results prove that the absorption coefficient of the breast tumor is higher than normal tissue due to higher vascularization level in four mentioned wavelengths. Conclusively, the results show 92 percent sensitivity for the extracted absorption coefficient.

This paper proposes a model predictive control structure to attain performance requirements and to meet with actuator constraints. The goal is to maintain the differential pressure between the anode and cathode sides of a polymer electrolyte membrane fuel cell (PEMFC), at the inlet side of the stack, known as fuel overpressure, in a desired region. The effects of dynamic purging, nitrogen crossover, and load are included as disturbances. A proportional control valve is used as an actuator. Applying the pneumatic modeling technique, the behavior of a Ballard 3kW test station in a dead-end anode configuration is replicated and experimentally validated. To achieve a linear model, a nonlinear transformation is used to decompose the valve dynamic behavior. To evaluate the controller performance, numerical simulation is conducted using data from the experimental model. The results show the ability of the controller to compensate for all disturbances.

A solution to the state estimation problem of systems with unmeasurable non-zero mean inputs/disturbances, which do not satisfy the disturbance decoupling conditions, is given using the Kalman filtering and Bayesian estimation theory. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called supervisory layer, numerically solves the measurement equations for the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter's last updated estimate. Then, the estimated measurement vector associated with each particle is compared to the actual measurement vector to identify the particle's probability to be a solution. Finally, a so-called resampling stage is implemented to refine the particles with higher likelihoods. The effectiveness of the SKF is demonstrated by comparing its estimation perform- ance with that of the standard Kalman Filter and the particle filter for a vehicle state estimation problem. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, regardless of the unknown disturbances from the road. The filtering methodology offered in the article has a potential to improve performance of the systems presented in the patents WO2011115960, WO2010024751, US8073528, and US20110299730.

This paper investigates the performance of a modified conventional solenoid valve as a low-cost controllable reciprocating force actuator. The applied modifications include adding a spring for plunger return mechanism, applying a bias current for pre-compression of the spring, and minor machining of the plunger rod. Potentially, this actuator can be employed in many applications, e.g. active noise and vibration control systems. A mathematical model of the actuator is given using the fundamental electromagnetic relationships. This model identifies the transfer function between the applied current and the output force. An experimental analysis in the frequency domain is conducted to obtain the current/force transfer function and validate the mathematical model. It is shown that in the stroke range of interest, the nonlinear analytical model of this actuator can be well estimated by a linear model. To investigate the performance of the actuator in a real application, this actuator is installed and tested in an active engine mount prototype. The analytical model of this engine mount is modified to include the actuator. The simulation result indicates that the stiffness and damping of this mount is tunable by controlling the input current to the actuator. The experimental results are also in close agreement with the simulations.

The conversion of rotary to translational motion in lead screw drives occurs at the meshing lead screw and nut threads. Adecreasing coefficient of friction with sliding velocity, may lead to instabilities in these drives. In this paper, a sliding mode controller is designed for a two degrees of freedom lead screw drive model. This controller has two objectives: to regulate the lead screw angular velocity to a preset value and to attenuate friction-induced vibrations. Only upper and/or lower bounds of system parameters are assumed to be known. In addition, in the development of the controller no specific model is assumed for the dependence of the coefficient of friction on the relative sliding velocity. Two modifications are applied to the basic discontinuous sliding mode controller to eliminate the inherent chattering problem and to limit thecontrolled input torque levels. Numerical simulation results are presented that show the applicability and the performance of the proposed controller.

This paper describes the design of a versatile and fully controllable active engine mount. The proposed active mount is capable of addressing vibration isolation requirements at various driving conditions. This design addresses a better ride quality that has always been demanded by the automotive industry, as well as satisfying sophisticated vibration isolation requirements for the unconventional engines, i.e. variable displacement, and hybrids. The proposed engine mount replaces the decoupler of the original design with a solenoid actuator. The mathematical model of the active mount is obtained. The dynamic characteristics of the mount are shown to be highly controllable over the operating frequency range of excitation in engines. The effectiveness of the developed active engine mount for various working conditions of engine is also evaluated. Several driving conditions are investigated and proper control strategies are utilized to demonstrate the mount's capability to fulfill the isolation requirements for each condition. The promising results, in addition to compactness, low cost, fail safety, and durability are the main advantages of the proposed active engine mount, which makes it viable for automotive applications.

The use of electromagnetic dampers (ED) in vehicle active suspension systems has drawn considerable attention in the past few years, attributed to the fact that active suspension systems have shown superior performance in improving ride comfort and road handling of terrain vehicles, compared with their passive and semi-active counterparts. Although demonstrating superb performance, active suspensions still have some shortcomings that must be overcome. They have high energy consumption, weight, and cost and are not fail-safe in case of a power breakdown. The novel hybrid ED, which is proposed in this paper, is a potential solution to the above-mentioned drawbacks of conventional active suspension systems. The proposed hybrid ED is designed to inherit the high-performance characteristics of an active ED with the reliability of a passive damper in a single package. The eddy current damping effect is utilised as a source of the passive damping. First, a prototype ED is designed and fabricated. The prototype ED is then utilised to experimentally establish the design requirements for a real-size active ED. This is accomplished by comparing its vibration isolation performance in a 1-DOF quarter-car test rig with that of a same-class semi-active damper. Then, after a real-size active ED is designed, the concept of hybrid damper is introduced to the damper design to address the drawbacks of the active ED. Finally, the finite-element method is used to accurately model and analyse the designed hybrid damper. It is demonstrated that by introducing the eddy current damping effect to the active part, a passive damping of approximately 1570 Ns/m is achieved. This amount of passive damping guarantees that the damper is fail-safe and reduces the power consumption more than 70%, compared with an active ED in an automotive active suspension system.

Semi-active systems improve suspension performance of the vehicles more effectively than conventional passive systems by simultaneously improving ride comfort and road handling. Also, because of size, weight, price and performance advantages, they have gained more interest over the active as well as passive systems. Probably the most neglected aspect of the semi-active on-off control systems and strategies is the effects of the added nonlinearities of those systems, which are introduced and analysed in this paper. To do so, numerical techniques, analytical method of averaging and experimental analysis are deployed. In this paper, a new method to analyse, calculate and compare the performances of the semi-active controlled systems is proposed; further, a new controller based on the observations of actual test data is proposed to eliminate the adverse effects of added nonlinearities. The significance of the proposed new system is the simplicity of the algorithm and ease of implementation. In fact, this new semi-active control strategy could be easily adopted and used with most of the existing semi-active control systems.

A novel neural fuzzy (NF) mapping framework is developed in this paper to convert linear systems and a class of nonlinear systems from the crisp-domain to a NF representation. The resulting neural fuzzy system (NFS) is guaranteed to be functionally identical to the original system. Therefore, the proposed mapping technique provides a well-defined prototype for one type of NFS design. The resulting fuzzy reasoning representation facilitates the investigation in linguistic terms into the system operations, whereas the system performance can be further improved by properly incorporating expertise knowledge or by online/offline training via this NF structure. The developed technique is to extend our previously-developed techniques to NF modeling/mapping applications and its effectiveness is demonstrated by simulations using a flexible-link robot.

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free.

This paper presents a novel methodology that estimates position and orientation using one position sensor and one inertial measurement unit. The proposed method estimates orientation using a particle filter and estimates position and velocity using a Kalman filter (KF). In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results show that the orientation errors using the proposed method are significantly reduced compared to the orientation errors obtained from an extended Kalman filter (EKF) approach. The improved orientation estimation using the proposed method leads to better position estimation accuracy. This paper studies the effects of the number of particles of the proposed filter and position sensor noise on the orientation accuracy. Furthermore, the experimental results show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown.

In this paper, a novel systematic and practical methodology is presented for design of vehicle semi-active suspension systems. Typically, the semi-active control strategies developed to improve vehicle ride comfort and stability have a switching nature. This makes the design of the controlled suspension systems difficult and highly dependent on an extensive trial-and-error process. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time and frequency design techniques, established in the conventional control system theory, can be applied. If the semi-active control system is designed to satisfy some ride and stability requirements, an inverse mapping offers the ultimate control law. At the end, the entire design procedure is summarised in six steps. The effectiveness of the proposed methodology in the design of a semi-active suspension system for a Cadillac SRX 2005 is demonstrated with road tests results. Real-time experiments confirm that the use of the newly developed systematic design method reduces the required time and effort in real industrial problems.

This paper investigates the vibrations of balanced fault-free ball bearings. A lumped mass-damper-spring model is adopted including the use of the Hertzian contact theory to represent the stiffness of the bearing rolling elements. We found that the equilibrium point of the bearing undergoes a supercritical pitchfork bifurcation as the bearing internal clearance increases. We developed closed-form expressions for the frequency-response functions of the horizontal and vertical motions of bearings with small internal clearance (below the bifurcation point). We also developed a chaos map to describe the locations and intensity of chaos in the internal clearance–shaft speed parameter space for bearings with larger internal clearance (beyond the bifurcation point).

Rolling-element bearings are widely used in various mechanical and electrical facilities; accordingly, a reliable real-time bearing condition-monitoring system is very useful in industries to detect bearing defects at both incipient and advanced levels to prevent machinery performance degradation and malfunctions. The objective of this paper is to develop an enhanced diagnostic (ED) scheme for bearing fault diagnostics. This scheme consists of modules of classification and prediction. A neurofuzzy (NF) classifier is proposed to effectively integrate the strengths of several signal-processing techniques (or resulting representative features) for a more positive assessment of bearing health conditions. A multistep NF predictor is employed to forecast the future states of the bearing health condition to further enhance the diagnostic reliability. The effectiveness of this ED scheme is verified by experimental tests that correspond to different bearing conditions.

A reliable predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive multi-step predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm for system state forecasting. A variable input pattern is proposed to improve the forecasting performance. A hybrid training algorithm, based on the recursive Levenberg–Marquardt algorithm and recursive least square estimate, is suggested to enhance forecasting convergence and to accommodate time-varying system conditions. The viability of the developed predictor is evaluated by simulations on both benchmark data sets and experimental data sets corresponding to machinery condition monitoring. The investigation results show that the developed adaptive predictor is a reliable and robust multi-step forecasting tool. It can capture and track system's response quickly and accurately. It outperforms other related classical forecasting schemes.

This paper utilizes an intelligent system which incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one inertial measurement unit and one position sensor to determine the orientation and the center of mass location of the tool. KFs are used to estimate the orientation of the tool and the center of mass location of the tool. Although a KF is used for the orientation estimation, orientation error increases over time due to the integration of angular velocity error. Therefore, a methodology to correct the orientation error is required when the system is used for an extended period of time. This paper proposes a method to correct the tilt angle and orientation errors using a fuzzy expert system. When a tool fastens a bolt, the system identifies the fastened bolt using a fuzzy expert system. Through this bolt identification step, the 3-D orientation error of the tool is corrected by using the location and orientation of the fastened bolt and the position sensor outputs. Using the orientation correction method will, in turn, result in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts.

Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable online bearing fault-diagnostic technique is critically needed to prevent the system's performance degradation and malfunction. In this paper, an extended wavelet spectrum analysis technique is proposed for a more positive assessment of bearing health conditions. Two strategies have been suggested for different wavelet function implementation. Two statistical indexes are proposed to quantify the resulting wavelet (coefficient) functions. Based on the information provided by these indexes, the wavelet functions can be deployed more effectively over the designated frequency bands. An extended Shannon function is proposed to synthesize the wavelet coefficients over selected bandwidths to enhance feature characteristics. An averaged autocorrelation power spectrum is adopted to highlight bearing characteristics. The viability of the developed technique is verified by online experimental tests corresponding to different bearing conditions.

This paper discusses the design of an eddy current passive damper using different configurations of permanent magnets. Motional eddy current damping effect is used for the development of a passive damper. Eddy currents are generated in a conductor in a time-varying magnetic field. They are induced either by movement of the conductor in a static field or by changing the strength of the magnetic field, initiating motional and transformer electromotive forces, respectively. The conceived eddy current damper consists of a conductor as an outer tube, and an array of axially magnetized, ring-shaped permanent magnets (PMs), separated by iron pole pieces as a mover. The relative movement of the magnets and the conductor causes the conductor to undergo motional eddy currents. Using this concept, damping characteristics of the new damper is obtained through analytical modeling, and verified by experimental analysis. The optimum PMs’ size and configuration are also derived using analytical and finite element analysis, respectively. A damping coefficient as high as 53 kg/s is achievable with the proposed design specifications.

This paper describes the design, modeling, and analysis of a novel magnetic spring–damper. This cost-effective, self-powered magnetic spring–damper utilizes two permanent magnets and a conductive aluminum plate to generate both spring and variable damping effects. Eddy currents are generated in the aluminum plate due to its relative motion with respect to the magnets. These eddy currents produce a repulsive force that is proportional to the velocity of the conductor such that the moving magnet and conductor act as a viscous damper. The structure of the proposed passive magnetic spring–damper is simple, and does not require an external power supply or any other electronic device. An accurate, analytical model of the system is obtained by using the electromagnetic theory to estimate the electromagnetic forces, induced in the system. The newly developed model can be used to design high-performance dampers for various applications. To optimize the design, simulations are conducted and the design parameters are evaluated. After a magnetic spring–damper prototype is fabricated, experiments are conducted to verify the accuracy of the theoretical model. The eddy current model exhibits a 7.5% RMS error for the damping ratio estimation, and a damping ratio as high as 40 N s/m is achieved by the fabricated prototype.

This paper proposes a neuro-fuzzy (NF) strategy to implement semi-active suspension in passenger vehicles. The proposed method is composed of two parts: a NF controller (NFC), to establish an efficient controller strategy to improve ride comfort and road handling (RCH), and an inverse mapping to estimate the semi-active suspension current. To effectively estimate the current needed to control the semi-active damper, an inverse mapping based on neural network, modified back-propagation (MBP) is presented. The inverse mapping is incorporated into the FC to enhance RCH. Given the relative velocity between the mass and the base and also the absolute acceleration of the mass, the FC computes the optimum damping coefficient. The fuzzy logic rules are extracted based on expert knowledge encapsulated in skyhook and groundhook. A quarter-car model was adopted for the purpose of simulating and experimenting with the proposed NFC. To verify the performance of the FC, two sets of results are reported. First, an exper...

In this paper we propose Periodic Dynamic Thermography (PDT), in conjunction with Image processing and Analysis, to be an easy to use procedure involving capturing thermal images of the breast. The authors present a proof of concept study for subjects to image their own breasts periodically, starting from early ages of adolescence, where mammography is not recommended. Utilizing the proposed procedure, it is envisaged that each subject will establish historical thermal images of their own breasts where they can later be analyzed, by a physician, to assess the causes of thermal changes identified on the surface of the breast. Towards this end we have utilized phantoms, solved the heat transfer equations for the phantom shape boundary conditions with COMSOL, and confirmed the resulting thermal distribution with the observed temperature distribution for the phantoms. Based on this model’s promising results we propose the development of a system to capture thermographic images of the subject from their early years in life, periodically (monthly or bi-weekly), with image analysis application software to be used as an assessment tool to alert the subject of any abnormal thermal changes.

This paper proposes a systematic methodology for predicting and optimizing the performance of an energy regenerative suspension system to efficiently capture the vibratory energy induced by the road irregularities. The method provides a graphical design guideline for the selection of stiffness and damping coefficients aimed at either best ride comfort or maximum energy harvesting. To achieve energy regeneration capability, a low-power electronic circuit capable of providing a variable load resistance is developed and fabricated. The circuit is controlled to provide an adjustable damping coefficient in the real-time. A test-bed is utilized to experimentally verify the proposed techniques. The results indicate that the analytical and simulation results concerning the optimal values for dynamic control and power regeneration match the experimental results.

This paper, presents an optimal control approach for maintaining the differential pressure between the anode and cathode sides, known as fuel over-pressure, in a polymer electrolyte membrane (PEM) fuel cell using model predictive control. In order to achieve the performance requirements, a proportional control valve is used and the state-space representation of the system is linearly approximated around each operating condition, and the overall model has been verified experimentally. Numerical simulation results are also provided to show the controller performance using experimental data from a 9-cell PEM fuel cell stack. These proof-of-concept results show the effectiveness of the controller to provide the optimal control input while satisfying the constraints of the problem.

The detection of the onset of damage in gear systems is of great importance to industry. In this paper, a new neuro-fuzzy diagnostic system is developed, whereby the strengths of three robust signal processing techniques are integrated. The adopted techniques are: the continuous wavelet transform (amplitude) and beta kurtosis based on the overall residual signal, and the phase modulation by employing the signal average. Three reference functions are proposed as post-processing techniques to enhance the feature characteristics in a way that increases the accuracy of fault detection. Monitoring indexes are derived to facilitate the automatic diagnoses. A constrained-gradient-reliability algorithm is developed to train the fuzzy membership function parameters and rule weights, while the required fuzzy completeness is retained. The system output is set to different monitoring levels by using an optimization procedure to facilitate the decision-making process. The test results demonstrate that the novel neuro-fuzzy system, because of its adaptability and robustness, significantly improves the diagnostic accuracy. It outperforms other related classifiers, such as those based on fuzzy logic and neuro-fuzzy schemes, which adopt different types of rule weights and employ different training algorithms.

A reliable machine fault prognostic system can be used to forecast damage propagation trend in rotary machinery and to provide an alarm before a fault reaches critical levels. Currently, there are several techniques available in the literature for time-series prediction. Among the most promising methods are recurrent neural networks (RNNs) and neuro-fuzzy (NF) systems. In this paper, the performance of these two types of predictors is evaluated using two benchmark data sets. Through comparison it is found that if an NF system is properly trained, it performs better than RNNs in both forecasting accuracy and training efficiency. Accordingly, NF system is adopted to develop an on-line machine fault prognostic system. In order to facilitate the automatic monitoring process, reference function approach is proposed here to enhance feature representation. The performance of the developed prognostic system is evaluated by using three test cases including a worn gear, a chipped gear, and a cracked gear, as well as using data sets from previous studies corresponding to a gear pitting damage and a shaft misalignment. From these tests, the NF prognostic system is found to be a very reliable and robust machine health condition predictor. It can capture the system dynamic behaviour quickly and accurately.

The main concern in printing quality in multistage presses is doubling. Doubling is caused by imperfections either within stages (units) or in links connecting different stages, mainly resulting from machine vibration, gear damage, and excessive run-out. In this paper, we propose new means for printing quality control via geared system health condition monitoring. The diagnosis is based on the signals acquired from inexpensive magnetic pickups. A new technique is developed to monitor the gear rotation synchronization among different stages in order to isolate possible sources of the doubling problem. A new approach is proposed to determine the gear run-out. Moreover, gear tooth damage detection is conducted using the beta kurtosis and the continuous wavelet transform based on the overall residual signal. The beta kurtosis of original signal average is also shown here to be useful in detecting excessive gear run-out. Test results from printing presses demonstrated the viability of the proposed methods.

This paper focuses on the development of a complete non-linear model of a hydraulic engine mount and the evaluation of the model using a unique experimental apparatus. The model is capable of capturing both the low- and high-frequency behavior of hydraulic mounts. The results presented here provide a significant improvement over existing models by considering all non-linear aspects of a hydraulic engine mount. Enhancements to already published non-linear models include a continuous function that follows a simplistic yet effective approach to capture the switching effect and leakage through the decoupler, and upper chamber bulge damping. It is shown that the model developed here provides the appropriate system response over the full range of loading conditions (frequency and amplitude) encountered in practice. In order to obtain the parameter values for the non-linear model, a unique test apparatus is introduced. Using the experimental set-up, it is possible to verify the model of individual components of the mount, and later on test the behavior of the whole assembly. These data also establish the relative importance of several damping, inertia and stiffness terms. In addition, the measured responses of the mounts to loading at various frequencies and amplitudes are compared to the predictions of the mathematical model. The comparisons generally show a very good agreement (better than 10%), which corroborate the non-linear model of the mount. It is felt that this work will help engineers in reducing mount design time, by providing insight into the effects of various parameters within the mount.

In this paper, a new switched-mode bidirectional bridgeless AC/DC converter, operating in the discontinuous conduction mode (DCM), for automotive energy harvesting is presented. The simple topology consisting of common source n-MOSFETs allows for direct AC/DC conversion, minimum auxiliary circuits and simple gating patterns, when compared to other AC/DC converters proposed in the literatures. The circuit operation is analyzed and shown that the converter-battery topology can operate as a synthesized variable resistor with regenerative functionality. The circuit is designed for low-power energy harvesting applications; for example, regenerating dissipated power in a vehicle suspension damper by converting vibrational energy into battery charge. Simulation results including various switching waveforms and power conversion efficiency are presented to demonstrate performance of the proposed converter. Experimental results, including power conversion efficiency and resistance synthesis sweep, by connecting the proposed converter to a regenerative vehicle suspension prototype are also included.

This paper proposes a mechatronic suspension system with capabilities of energy regeneration and sky- hook (SK) control incorporated into the mechanism. The system overcomes the tradeoffs between energy consumption and ride comfort in an active suspension system. Central to the concept is development of a coupled switched-mode rectifier (SMR) capable of providing either a positive or negative damping ratio by alternating between regenerative and motoring modes. Using the proposed circuit, an active sky-hook control strategy is utilized to offer continuous damping force that improves the vibration isolation significantly. Simulation results presented demonstrate the directions of power flow in both regenerating and motoring modes to verify performance of the SMR.

Each gear damage monitoring technique has its merits and limitations. This paper experimentally investigates the sensitivity and robustness of the currently well-accepted techniques: phase and amplitude demodulation, beta kurtosis and wavelet transform. Four gear test cases were used: healthy gears, cracked, filed and chipped gears. The vibration signal was measured on the gearbox housing and processed, online, under three filtering conditions: general signal average, overall residual and dominant meshing frequency residual. Test results show that beta kurtosis is a very reliable time-domain diagnostic technique. Phase modulation is very sensitive to gear imperfections, but other information should be used to confirm its diagnostic results. Continuous wavelet transform provides a good visual inspection especially when residual signals are used. The diagnosis based only on dominant meshing frequency residual, however, should not be used independently for gear health condition monitoring, it may give false alarms.

This paper concerns nonlinear modeling of a hydraulic engine mount, focusing on the decoupler action. The proposed model is a simple nonlinear function, which describes the flow resistance in terms of the decoupler plate displacement and speed. The flow resistance using this model increases dramatically as the decoupler plate approaches the top or bottom of the decoupler cage. The advantage of the proposed model over the existing ones is its simplicity. Numerical simulation results show the behavior of the model to be realistic. Approximate closed form solutions have been obtained for the system using the multiple scale perturbation method. Perturbation provides a solution for the mount response at resonance. These results match the intuitive and experimental results available in the literature.

The newly developed wavelet transform enables one to look at the evolution in time of a signal's frequency content. This property makes it very suitable for the detection of vibration transients generated by developing localised faults in gear trains. Recent applications of the wavelet technique have focused on the use of the amplitude map only. In this study, both the amplitude and phase maps of the wavelet transform are used in conjunction to assess the condition of an instrumented gear test rig. The phase wavelet map is found to display distinctive features in the presence of a cracked tooth. Another key finding is that the amplitude wavelet map of the residual vibration signal offers a better indicator to the presence of faults than the map of the actual signal. A new polar representation is also introduced that makes pinpointing the precise location of a crack an easy task. The cases studied include a healthy gear, one with a simulated fatigue crack, and one with a simulated chipped tooth.

This paper is an experimental study of free and forced vibration suppression in a piezoceramic actuated flexible beam via the nonlinear Modal Coupling Control (MCC). The method is based on transferring the oscillatory energy from the plant to an auxiliary second order system (controller), coupled to the plant through nonlinear terms. The proposed controller produces an input that can be utilized by unidirectional actuators. Unidirectional actuators do not generate symmetric power during an application. Shape memory alloys, thrusters and cable based actuators are examples of this class of actuators. Existing control methods assume a symmetric actuation and therefore application of unidirectional actuators call for new control techniques. Current control techniques implement a bias in utilizing unidirectional actuators. However, the amount of the bias is variable and depends on the control effort. Moreover, the use of a bias changes the system equilibrium point and introduces a steady state error. The proposed controller is used to control the first mode of a flexible beam. The results show that the method is more effective than conventional control approaches in conjunction with unidirectional actuators.

A novel approach for implementing an active nonlinear vibration absorber is presented. The absorber, which is built in electronic circuitry, takes advantage of the saturation phenomenon that occurs when two natural frequencies of a system with quadratic nonlinearities are in the ratio of two-to-one. When the system is excited at a frequency near the higher natural frequency, there is a small ceiling for the system response at the higher frequency and the rest of the input energy is channeled to the low-frequency mode.