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As promising non-destructive testing (NDT) method, magnetic flux leakage (MFL) testing has been widely applied for steel structure detection. However, magnetic flux leakage (MFL) signal collected by the sensors is weak, especially for the small diameter wire rope. The methods of using the traditional circular coil sensors, or increasing the number of magnets is ineffective in some situations. This study proposes a new method of highly sensitive long-strip-shape coil sensors with an analogue signal processing circuit used to detect wire rope in the timing belt of wind turbines. It is investigated by theory analysis, circuit simulation, and experiment conduction. The circuit is used to amplify the weak signal and filter high-frequency noise to obtain a high signal-to-noise ratio (SNR). Experiments show that the resolution of the method can reach 3 broken wires of 0.1 mm wire diameter, and the method can not only obtain a high SNR signal but also have good repeatability. Meanwhile, this method will have great significance in practical applications and provide some solutions for other similar NDT projects.

The traditional magnetic flux leakage (MFL) method with a single magnetic excitation source has been widely used in engineering practice but fails in long-range (long liftoff distance of the sensor from the surface of the tested object) defect detection, especially when the liftoff distances increase. Thus, we propose a novel electromagnetic testing method based on the magnetic field interaction between the primary magnetic induction field and the secondary field of MFL. The principles of the new method are first introduced, and the method is compared with the common MFL methods; this comparison indicates that the new method has the capabilities of long-range defect detection and signal strength enhancement. Furthermore, a series of simulations and analyses demonstrates that the proposed method is more efficient in defect detection, particularly when the induction magnet is a diamagnetic material, which is independent of the coercive force. Finally, experiments and comparisons were conducted to verify the feasibility and reliability of our proposed method in the long-range defect detection. All results show that the novel method has great promise in widely practical applications in the future.

As the precision of the manufacturing process and the quality of ferromagnetic parts are further enhanced, surface topography is playing a significant role in guaranteeing the service performance and lifetime of mechanical parts. Thus, a novel magnetic focusing method (MFM) that is not susceptible to a hostile environment in a sophisticated and online manufacturing process is proposed, which is demonstrated to be an effective and feasible technique with higher detection resolution and a higher signal-to-noise ratio (SNR). By a theoretical introduction of the magnetic focusing mechanism and finite element simulations and calculations for six main MFM sensor structural parameters, the MFM sensor characterizations and optimal structural parameters are presented. Furthermore, the feasibility and validity of the proposed novel method and sensor in surface topography inspection for ferromagnetic materials were verified in the experiments. Finally, the advantages and disadvantages of the common surface topography testing methods were compared. These results demonstrate the great promise in the application of MFM and the corresponding sensor in practical engineering.

The purpose of spectral refinement is to obtain a finer spectrum structure so as to realize a more accurate spectral measurement. In this paper, a micro frequency-shift based spectral refinement algorithm (MFS-FFT) is introduced. This algorithm can efficiently perform any multiple spectral refinement and achieve the refinement over the entire frequency axis without band selection. Two kinds of micro frequency shifters suitable for this algorithm are discussed. The influence of window functions on this algorithm is also analyzed. Compared with the Chirp Z-transform (CZT), the proposed algorithm can achieve the same accuracy as CZT. Our complexity analysis has shown that when the refinement multiple is less than six, the computational cost is always smaller than that of CZT. A spindle speed ramp-up milling experiment has been performed in order to verify the proposed algorithm. Experimental results show that the MFS-FFT algorithm can effectively improve the accuracy of spectral estimation.

Aiming to the current situation that single axial magnetization can hardly qualified for omnidirectional defects detection, meanwhile, conventional complex magnetization method features with low speed and low efficiency. Thus, a novel method of magnetic flux leakage (MFL) testing for omnidirectional defects based on super-strong single axial magnetization as well as the corresponding detection apparatus were proposed. First, principles of single axial magnetization method were introduced, where a through-coil with high direct current was mainly used to generate the super-strong magnetic field. Then, methods of omnidirectional defects detection through magneto sensor arrays were concretely presented. What’s more, details of equation sets for lateral and longitudinal defects detection and classification were given. Additionally, a summation and comparison combination method was put forward and different defect signal amplitudes were compensated. Naturally, a unified judgment for omnidirectional defects inspection with same sensitivity or accuracy was formed. Most importantly, series of simulations and experiments were conducted, and finally, the feasibility and reliability of the new MFL testing method were demonstrated by those results. The obvious advantages for the suggested method are that it not only makes the detection for omnidirectional defects under single axial magnetization possible, but also the low economic cost, high speed and high efficiency detection for both lateral and longitudinal defects adaptable for various shaped ferromagnetic objects with great application promise in engineering is ultimately accomplished.

We present a new analytical method for solving the problem of relative camera pose estimation. This method first calculates the homography matrix between two calibrated views using unknown coplanar points, and then, it decomposes the matrix to estimate the relative camera pose. We derive a set of new analytical expressions that are more concise than other homography decomposition methods. These analytical expressions are also used to improve the efficiency of a traditional SVD-based homography decomposition method. The performance of our analytical method is studied in terms of both efficiency and accuracy, and it is compared with other homography decomposition methods. Furthermore, the accuracy of our analytical method is tested under different conditions and compared with that of the five-point method through simulations and real image experiments. The experimental results demonstrate that our method is faster and more accurate than other homography decomposition methods and more accurate than the five-point method.

The current methods of electric–magnetic flux leakage structural health monitoring that use tubular coils as electro-magnetizers fail to detect concrete rebar structures without a head and a tail. This article proposes a novel electric–magnetic flux leakage structural health monitoring technique for concrete rebar using open electromagnetic coils as magnetizers. Three-dimensional finite element simulations and experiments are conducted to compare the magnetization effect, defect-detection capability, and magnetic force interaction of traditional tubular coils and the proposed open electromagnetic coils. The agreement between the simulation and experimental results confirms the reliability and validity of the proposed structural health monitoring technique for concrete rebar. Additionally, two types of magnetizer coils are designed and manufactured using optimized structures, and both limitations and solutions are discussed. Overall, the proposed open magnetizer coils show great potential for future structural health monitoring applications for concrete rebar.

Based on the authors’ previous work of magnetic flux leakage (MFL) quantitative description and the research plan especially for the magnetic component of eddy current effect factor $$ B_{other} $$, further explorations for electromagnetic actions, magnetic and signal presentations in the defect in electromagnetic nondestructive testing (ENDT, such as direct current field measurement (DCFM), MFL and eddy current testing (ECT)) are continued here. Primarily, new discoveries indicate that the magnetic component of $$ B_{other} $$ is actually composed of the secondary magnetic field component of $$ \Delta B_{mfl} $$ which manifests as raised signal waves and volume current perturbation component of $$ \Delta B_{\Delta I} $$ which features as double peaked signals due to the departure or bypassing of the “volume current” in the defect. What’s more, the concrete expressions of magnetic components are deduced and given through Ampere’s (or the Ampere–Maxwell) law. Afterwards, the theory is well demonstrated and verified by means of finite element analysis for current density and magnetic flux density or MFL signals in typical quasi-static testing by DC magnetization for different specimens with defects. Additionally, suggestions for installation site of sensor elements in ENDT are also given. Finally, a unified evaluation standard for magnetic flux density is formed, which provides the basic theory or analysis principles for the ENDT techniques as well as research directions for precision defect detection and related applications.

Wire rope is a necessary tool in practical applications especially in crane, elevator and bridge construction, which plays an important role in the national economy and daily life, and safety inspection for wire rope is the key to ensure people’s life and property. However, detection signals are usually complicated due to the twining structures, which make the wire rope defect signal and strand signal mix together. What’s more, no reports and studies have appeared to solve this problem. In view of the situation and challenges above, this paper proposes a combined signal processing method based on notch filtering and wavelet denoising to process detected wire rope signals. Basic time domain, frequency domain and joint time–frequency analysis are first conducted, thereafter, conventional signal processing methods such as lowpass filtering and adaptive analysis are presented according to the signal characterizations. These comparisons and results demonstrate that a conventional single method is incapable of wire-rope-detection signal identification and differentiation. Nonetheless, after the notch filter design and calculation, the processing results for the typical wire rope inspection signals in the experiments indicate that the combined methods can not only distinguish steel wire rope defect signal and strand signal effectively but also with high detection accuracy, even for the inner defect. Finally, the feasibility and reliability are verified by a series of signal processing results and comparisons, which demonstrate that this new method has great application potential and is of vital significance to the development of wire rope safety inspection.

This paper analyzes the inspection characteristics of the alternating current electric flux leakage (AC-EFL) testing method. Three specimens with different conductivity are prepared, and a series of experiments is carried out to explore the advantages and disadvantages of the AC-EFL method. For metal materials that carry an alternating current (AC), defect detection can be realized using both AC-EFL and electric current perturbation (ECP). However, the signal noise ratio (SNR) from using the AC-EFL method is lower than that obtained using the ECP method according to the experimental results obtained from an aluminum plate. For both the Ni–Zn ferrite specimens with very low conductivity and the carbon fiber reinforced polymer (CFRP) specimens with low anisotropic conductivity, the ECP method failed to detect defects because of the weak disturbed magnetic field that was caused by the defects, whereas the AC-EFL method was able to realize the defect detection. These proof-of-concept experimental results indicate that compared to magnetic field testing method, the AC-EFL is more suitable for inspecting low-conductivity materials.

In this paper, bear fault is automatically diagnosed by using pattern recognition. To improve the resolution of lower frequency part, we introduce scale factors to discrete wavelet composition (DWT). The modified DWT combined with high order cumulates are used for vibration signal feature extraction. Besides we use principle component analysis to reduce dimension of the feature data. This feature extraction method has a lower dimension and a higher resolution for lower frequency parts. Therefore it can not only reveal the characteristics of non-linear relationship between amounts of features, but also help to improve the speed and accuracy of classification. Finally neural network algorithm is used for fault classification. Result shows that our method can accurately and efficiently identify the type of bearing failures.

This paper improves the learning dictionary construction method for morphological component analysis (MCA) to separate the atrial and ventricular signals. The incoherence is added into the objective function to reduce the sparsity ratio between the atrial and ventricular dictionaries. By using the dictionaries, atrial and ventricular activities are separated from the location of the coefficients. We test the methods on both the synthetic and real atrial data. While extracting AFW from synthetic data, we use the Poisson relation as the measure. The result shows that we can obtain greater relation value using the method this paper presents than using the methods of ABS and PCA. We also conduct spectral analysis on AFW extracted from real atrial data.

The promising potential of cloud computing and its convergence with technologies such as cloud storage, cloud push, mobile computing allows for creation and delivery of newer type of cloud service. Combined with the thought of cloud computing, this paper presents a cloud-based remote measurement and analysis system. This system mainly consists of three parts: signal acquisition client, web server deployed on the cloud service, and remote client. This system is a special website developed using asp.net and Flex RIA technology, which solves the selective contradiction between two monitoring modes, B/S and C/S. This platform supplies customer condition monitoring and data analysis service by Internet, which was deployed on the cloud server. Signal acquisition device is responsible for data (sensor data, audio, video, etc.) collection and pushes the monitoring data to the cloud storage database regularly. Data acquisition equipment in this system is only conditioned with the function of data collection and network function such as smartphone and smart sensor. This system’s scale can adjust dynamically according to the amount of applications and users, so it won't cause waste of resources. As a representative case study, we developed a prototype system based on Ali cloud service using the rotor test rig as the research object. Experimental results demonstrate that the proposed system architecture is feasible.

Mobile health has been a new trend all over the world with the rapid development of intelligent terminals and mobile internet. It can help patients monitor health in-house and is convenient for doctors to diagnose remotely. Smart-phone-based mobile health has big advantages in cost and data sharing. Front end design of it mainly focuses on two points: one is implementation of medical sensors aimed at measuring kinds of medical signal; another is acquisition of medical signal from sensors to smart phone. In this paper, the above two aspects were both discussed. First, medical sensor implementation was proposed to refer to mature measurement solutions with ECG (electrocardiograph) sensor design taken for example. And integrated chip using can simplify design. Then second, typical data acquisition architecture of smart phones, namely Bluetooth and MIC (microphone)-based architecture, were compared. Bluetooth architecture should be equipped with an acquisition card; MIC design uses sound card of smart phone instead. Smartphone-based virtual instrument app design corresponding to above acquisition architecture was discussed. In experiments, Bluetooth and MIC architecture were used to acquire blood pressure and ECG data respectively. The results showed that Bluetooth design can guarantee high accuracy during the acquisition and transmission process, and MIC design is competitive because of low cost and convenience.

Feature extraction is a crucial aspect of computer-aided arrhythmia diagnosis using an electrocardiogram (ECG). A location, width and magnitude (LWM) model is proposed for extracting each wave's features in the ECG. The model is a stream of Gaussian function in which three parameters (the expected value, variance and amplitude) are applied to approx- imate the P wave, QRS wave and T wave. Moreover, the features such as the P-Q intervals, S-T intervals, and so on are easi- ly obtained. Then, a mixed approach is presented for estimating the parameters of a real ECG signal. To illustrate this mod- el's associated advantages, the extracted parameters combined with R-R intervals are fed to three classifiers for arrhythmia diagnoses. Two kinds of arrhythmias, including the premature ventricular contraction (PVC) heartbeats and the atrial prema- ture complexes (APC) heartbeats, are diagnosed from normal beats using the data from the MIT-BIH arrhythmia database. The results in this study demonstrate that using these parameters results in more accurate and universal arrhythmia diagnoses.

In vision measurement applications, it is usually used to achieve an optimal result by combing different processing steps and algorithms .This paper proposes a component based software framework for vision measurement. First, commonly used processing algorithms of vision measurement are encapsulated into components that contained in a components library. The component which is designed to have its own properties also provides I/O interfaces for extern calls. Second, a software bus is proposed which can plug components and assemble them to form a vision measurement application. Besides components managing and data line linking, the software bus also provides service of message distribution, which is used to drive all the plugged components working properly. Third, a XML based script language is proposed to record the plugging and assembling process of a vision measurement application, which can be used to rebuild the vision measurement application later. At last, based on this framework, an application of landmark extraction that applied in camera calibration is introduced to show how it works.

Distributed collaborative measurement is absorbing more and more research focus for its requirements in modern collaborative producing enterprises for low-cost products and short time to market. In this paper, we propose a distributed collaborative measurement pattern, which is built on wavefront array model. In this model, the architecture is viewed as a global array of measurement resources, and the resources are configured by web middleware, which can dynamically construct distributed measurement instrument to carry out the distributed collaborative producing cases. Based on the wavefront model, the measurement data can propagate among the configured modes on the array asynchronously. The measurement resources are distributed in the collaborative enterprises and the arranged as the virtual address of the array, so measurement cases can reconfigured based on the requirements and resource services, and the objective data and the processing results can be transferred sequentially to the other measurement units configured on the array. Finally, a wavefront array based distributed platform are present and a measurement cases are given. The application cases shows that the proposed ways can accelerate the collaboration of production for different enterprise and improve the producing efficiency.

Dynamic Reconfiguration is absorbing more and more research focus for its demand in automatic measurement system for low-cost products and short time to market. In this paper, we propose a software architecture, which is built on component and software bus models. In our architecture, the measurement system software is viewed as an integration of a set of reusable components, which are modelled with routing workflow for measurement, data process logic for execution of behavioural specifications and a series of communication ports for data exchange with others. The behaviours of the measurement system software can be viewed as an integration of components and their interactions and specified in application specification, which is based on routing workflow and independent of component implementation. When the measurement system needs to be reconfigured to adapt to the changing application requirements, the software using our architecture can be easily reconfigured by changing reusable components and their interactions in application specification and reconfiguration can be achieved at the executable code level after the software is implemented. Finally, a reconfigurable measurement platform bases on above software architecture for testing engineering signal has been developed and the goal of higher reconfigurability, lower development and maintenance costs are achieved with the proposed architecture.

Reconfigurable software is highly desired in automatic measurement system for low-cost products and short time to market. In this paper, we propose a software architecture, which is built on top of component and hierarchy software bus models. In our architecture, the measurement system software is viewed as an integration of a set of reusable components, which are modelled with a set of event-based extern interfaces for functional definitions, internal interfaces for modification of own properties, a data process logic for execution of behavioural specifications and a series of communication ports for data exchange with others. The behaviours of the measurement system software can be viewed as an integration of components and their interactions and specified in application specification, which is based on extend markup language (XML) and independent of component implementation. When the measurement system needs to be reconfigured to adapt to the changing application requirements, the software using our architecture can be easily reconfigured by changing reusable components and their interactions in application specification and reconfiguration can be achieved at the executable code level after the software is implemented. Finally, a reconfigurable measurement platform bases on above software architecture for testing engineering signal has been developed and the goal of higher reconfigurability, lower development and maintenance costs are achieved with the proposed architecture.