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As a key testing equipment in the aerospace field, turntables may experience resonance during testing, causing interruptions and unnecessary losses in the testing process. Traditional turntable resonance detection method has problems such as dependence on engineering experience, susceptibility to control signal interference, and insufficient accuracy, resulting in false alarms and missed alarms. Aiming at the problems of traditional turntable resonance detection method, a mechanical resonance detection method for turntables based on motor current data was proposed. Firstly, the operating data of a three-axis vertical turntable was analyzed, and the amplitude frequency characteristics of the turntable operating data were studied. The motor current signal with less control signal interference and more significant resonance characteristics was selected as the detection object. Then, a turntable resonance detection method based on convolutional neural network (CNN) was constructed, and the CNN structure and parameters were designed, followed by network training and learning. The network structure and parameters were optimized through experiments to achieve better recognition results. Finally, resonance detection experiments were conducted using data from the turntable operating in different working modes, and the results were compared with support vector machine, long short-term memory neural network and gated recurrent neural network. The research results show that the accuracy of this method in detecting turntable resonance reaches 99. 988%, which is better than other comparative methods, proving that this method is suitable for mechanical resonance detection of turntables. This method can be extended to the resonance detection of other equipment with motor. [ABSTRACT FROM AUTHOR]

Propellers comprise fan-shaped structures with spiral blades. The real-time measurement of cutting and grinding forces during the milling and grinding of propellers is difficult to achieve. The spindle motor current is a common parameter used to measure cutting force. However, data related to this parameter are not accessible during the use of DC motors for cutting processes and can be obtained only from servo motor drive systems. Given these considerations, in this study, we adopted LabVIEW software to develop a program for use in combination with an inexpensive sensor to achieve the simultaneous measurement of the cutting force and motor current. The program was verified to accurately depict the relationship between the cutting force and the motor current of reconfigurable precision five-axis machine tools, rendering it a viable alternative to measurement schemes that rely on expensive force sensors. Subsequently, the developed program was further tested by measuring changes in spindle motor current while cutting three blades to produce a 3D-printed polylactic acid propeller. [ABSTRACT FROM AUTHOR]

Laundering is essential for maintaining personal hygiene but can lead to fabric damage, such as color fading and fiber loss, caused by frequent and excessive mechanical action. Front-loading washers utilize mechanical force such as falling, rotating, and sliding to remove soil from textiles, where fabric properties influence these movements, resulting in varied mechanical actions depending on fabric type. Herein, the relationship between fabric properties and movement pertaining to mechanical action was analyzed, focusing on correlated fabric movements and motor torque current parameters. The results demonstrate that fabric properties, such as wet friction, moisture regain, thickness, and stiffness, significantly influence fabric movement in laundering. Moisture regain was associated with fabric movement, causing hydrophilic fabrics to undergo concentrated and repeated movements, resulting in higher rates of thread removal. In addition, heavier, thicker, and stiffer fabrics displayed dynamic changes in fabric shape, contributing to increased Poka-Dot removal. The motor current data required for rotating the drum was analyzed in association with the fabric properties. Using the current parameters as internal nodes of a decision tree model, fabric types were successfully classified. The results imply that the real-time current data and laundry movement information can be utilized for optimizing washing technologies for different fabric types, ultimately maximizing washing efficiency and minimizing fabric damage. [ABSTRACT FROM AUTHOR]

Motor current signal analysis is a common method that can be applied to the monitoring and diagnosis of electromechanical equipment. Three-phase unbalance will increase the line power loss and affect the safe operation of power-using equipment. To quickly analyze motor current three-phase unbalance, we propose a method to analyze three-phase unbalance by calculating the coefficient of variation of the three-phase current's instantaneous frequency and instantaneous amplitude. Simulations were conducted to analyze the effects of amplitude deviation and phase deviation on the coefficient of variation of instantaneous frequency and instantaneous amplitude. The three-phase unbalance with continuous amplitude change was simulated and its instantaneous performance was analyzed. Experiments were conducted to compare and analyze the instantaneous information characteristics of the motor with the continuous changing of three-phase unbalance under different speed conditions. Simulations and experiments show that the proposed method in this paper can analyze three-phase unbalance. [ABSTRACT FROM AUTHOR]

This paper proposes, investigates, and validates, by comprehensive experiments, new online automatic diagnostic technology for belt conveyor systems based on motor current signature analysis (MCSA). Motor current signature analysis (MCSA) is a method employed for detecting faults in electric motors by analyzing the current waveforms generated during motor operation. The technology capitalizes on the fact that motor defects, such as mechanical misalignment, bearing damage, and rotor bar defects, cause variations in a motor's current waveforms, which can be discerned and analyzed using advanced signal processing techniques. MCSA is a non-invasive and cost-effective technique that can detect motor faults in real-time without requiring expensive equipment or disassembly of the motor. In this study, the researchers tested the proposed diagnostic technology, which relies on a power feature. The power feature is calculated as the integrated power within a specific frequency range, centered around the fundamental harmonic of the supply frequency. The purpose of the study is to evaluate for the first time the effectiveness of the proposed diagnostic technology for the diagnosis of a tracking of a belt conveyor. The proposed technology's effectiveness is assessed using current signals that are obtained for two different scenarios: the normal belt tracking, and a belt mis-tracking under two different loads of a belt conveyor system. The study's findings indicate that the proposed technology has a high level of diagnostic effectiveness when used for belt mis-tracking. Therefore, it is feasible to recommend this technology for diagnosing tracking issues in belt conveyors. [ABSTRACT FROM AUTHOR]

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Hardened AISI 4140 steel is widely used in automotive industries due to its high strength, high hardness, and good formability. It can be subjected to cryogenic treatments to increase wear resistance, increase the toughness and decrease the residual stress. Although many studies have been performed related to the machinability of AISI 4140 steel, the number of studies that considered four responses simultaneously are limited. In this study, the effects of cutting speeds, feed rates, and depths of cut on the vibration, motor current, machining noise, and surface roughness were investigated in turning hardened AISI 4140 steel using coated carbide insert under dry cutting conditions. Besides, the attempt was made to predict surface roughness based on machining noise, motor current, and chuck vibration during the turning process. Analysis of variance and response surface methods were carried out to analyze the experimental results and the prediction models were developed. The machining test revealed that the most effective parameter on the surface roughness value was the feed rate with a 96.07% contribution. The depth of cut and the feed rate affected vibration with 57.61% and 26.44% contribution, respectively. The machining noise was dominantly affected by the depth of cut with 76.15% contribution, it was followed by feed rate with 17.67% contribution. However, feed rate and depth of cut significantly affect motor current with 54.53% and 36.64% contribution, respectively. The graphical analysis of the effect of vibration, motor current, and machining noise on the surface roughness was presented. Based on the results, there is a direct relationship between machining noise, vibration, and motor current with surface roughness. As the level of the aforementioned responses increases the quality of the machined surface deteriorates. [ABSTRACT FROM AUTHOR]

Monitoring transmission systems is a huge scientific focus to prevent any anomaly and malfunctioning damaging the system. Several methods were used to investigate the gears behaviour and mainly its state. And until the last century, vibrations signals were the most performing technique in this field. However, nowadays, other alternatives are considered more accurate and accessible such as controlling the motor current signals to study the behaviour of the mechanical system. Within this context, this paper aims to study the electromechanical interaction between a double stage of planetary gearboxes driven by an asynchronous machine. The model used is based on a Park transformation for modelling the asynchronous machine and a torsional model to describe the dynamic behaviour of the double-stage planetary gearbox. Through this approach, the numerical simulations illustrate the impact of the tooth gear defect on the signature of the motor current. The results obtained from the simulations will be presented in the time domain and the frequency domain using the fast Fourier transform and the Hanning window to highlight the mechanical frequencies in the phase current spectrum. This work will be distinguished by validating the numerical results using experimental measurements, which will be displayed in order to justify the sensitivity of the model developed.

Motor current signal analysis is a common method that can be applied to the monitoring and diagnosis of electromechanical equipment. Three-phase unbalance will increase the line power loss and affect the safe operation of power-using equipment. To quickly analyze motor current three-phase unbalance, we propose a method to analyze three-phase unbalance by calculating the coefficient of variation of the three-phase current’s instantaneous frequency and instantaneous amplitude. Simulations were conducted to analyze the effects of amplitude deviation and phase deviation on the coefficient of variation of instantaneous frequency and instantaneous amplitude. The three-phase unbalance with continuous amplitude change was simulated and its instantaneous performance was analyzed. Experiments were conducted to compare and analyze the instantaneous information characteristics of the motor with the continuous changing of three-phase unbalance under different speed conditions. Simulations and experiments show that the proposed method in this paper can analyze three-phase unbalance.

Machines and their components wear down over the course of time. A cause for increased wear can be misalignment issues that stay undetected and cause follow-up damages. Therefore, online monitoring of machines in the industry is of great importance. In this study, the axial misalignment occurring in different dimensions is determined. For this, a time frequency-based Short Time Fourier Transform (STFT) signal processing algorithm is used. The aim of the study is to diagnose faults without using sensors and external data collection systems, but rather by capturing motor current data from the servo motor of the machine. [ABSTRACT FROM AUTHOR]

This study aims to increase machine reliability thereby preventing product defects from adhesive dispense and slider attachment by a fault detection and diagnostic technique. The experiment was set up to investigate the vibration signal and motor current. Six fault conditions of a linear bearing were set up. The approaches, including spectrum analysis, crest factor, and analysis of variance, are used for data analysis. It was found that the spectrum analysis was suitable for classifying the frequency domains and the statistics tool was successful in measuring the current. Fault detection and diagnosis results can forecast the status of the linear bearings.

This study aims to increase machine reliability thereby preventing product defects from adhesive dispense and slider attachment by a fault detection and diagnostic technique. The experiment was set up to investigate the vibration signal and motor current. Six fault conditions of a linear bearing were set up. The approaches, including spectrum analysis, crest factor, and analysis of variance, are used for data analysis. It was found that the spectrum analysis was suitable for classifying the frequency domains and the statistics tool was successful in measuring the current. Fault detection and diagnosis results can forecast the status of the linear bearings. [ABSTRACT FROM AUTHOR]

In recent years, the collaborative robots have been widely used in vast fields, like aerospace, industrial, medical and household. Therefore, in robot collision detection, it is more and more important to ensure the safety of robot system. Compared with the traditional methods of installing sensors, a collision detection algorithm based on neural network (NN) is proposed. The current of the joint is estimated by neural network, and the threshold value is set to judge the occurrence of collision. This algorithm does not need to use the dynamic model of the robot, nor the acceleration. Moreover, the algorithm can be easily applied to existing systems without changing the structure of robots, which reduces the production cost. And a prototype with one degree of freedom (DOF) has been built to verify the validity of the algorithm. [ABSTRACT FROM AUTHOR]

In this study, effects of spherical clearance joint and flexible joint connection on the mechanism vibration are presented. A spatial mechanism model is selected for the experimental investigations. Different clearance values, a multi-axes flexible connection having cylindrical cross section and driving speeds are considered for the case studies. The chaotic phenomenon on the system dynamics arising from mainly spherical clearance joints is measured from the experimental test rig. In addition, the irregular reflections of the considered parameters on the actuator current are investigated. The positive contributions of flexible connection are evaluated upon the loss of kinematic constraints between the clearance joint parts. The experimental outputs show that the clearance-induced vibration makes the dynamics of system worse. The flexible connection between the adjacent mechanism links offers an important solution for minimizing the negative effects of clearance joint. Even if the same working parameters of mechanisms are considered, clearance joint fully affects the actuator current demand. Diameter of flexible connector can be arranged as a design variable to compensate the undesirable effects of clearance joint on the vibration responses and the actuator current demands. [ABSTRACT FROM AUTHOR]

• Adaptive parameter selection variational mode decomposition was proposed to diagnose bearing faults. • A new hybrid entropy, which includes the envelop fuzzy and discrete properties of the entropy was proposed. • Hybrid entropy is used to describe the complexity and detection dynamics of the intrinsic mode function of VMD. • The sensitive intrinsic mode function can be chosen by the novel hybrid entropy based on its features. The emission signal of locomotive bearings is non-stationary, nonlinear and mixed with complex noise when operating online. The adaptive decomposition method is widely employed for denoising and feature extraction. Variational mode decomposition (VMD) is a powerful adaptive decomposition method, but the number of modes K and the penalty factor α are difficult to set in advance and the unreasonable parameter selection may probably bring faulty feature loss. To address this problem, a novel hybrid entropy, which includes the envelope fuzzy and dispersion characteristics of entropy, is proposed to describe the complexity and detect dynamic change of intrinsic mode functions component of VMD. Furthermore, an adaptive parameter selection variational mode decomposition (APSVMD) based on the novel hybrid entropy is presented to obtain the optimal parameter combination (K and α). Then the sensitive intrinsic mode function (IMF) can be chosen by novel hybrid entropy based on its new features. In order to verify the performance of the proposed diagnostic approach, the locomotive online running test was performed. The experimental results demonstrated that the APSVMD represents the signal modes explicitly and can process different types of signals. As a result, different kinds of bearings faults were diagnosed successfully. [ABSTRACT FROM AUTHOR]

Noise and vibration sources from small direct current (DC) motors should be clearly visualized for optimal design of low noise motors. For accurate visualization, relatively good reference measurements at optimal locations are required. For some very small motors, the optimal position for a stationary reference microphone may not be accessible during measurement. However, strategies for small motor noise visualization without using a reference microphone have been developed in this study. Only scanning microphones and current measurements of a small motor were used to visualize sound sources. Scanning microphone signals combined with current measurements were used as moving reference signals. Motor noise visualization results based on different moving reference locations have been estimated and reported. Consistent motor noise visualization results from motor current and different, moving reference locations for the major electro-magnetic force excitation frequencies have been shown. Furthermore, for frequencies with relatively low current amplitude, clear motor noise visualization results have been produced for a moving reference located at the center of the motor. Also, the relationship between motor noise and current has been shown, and motor noise has been reduced by connecting an optimal capacitor to the motor power input.

In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system. [ABSTRACT FROM AUTHOR]

The dynamics of a machine tool ball screw is an important topic in high-precision machining. Capturing the axial and torsional dynamics of the ball screw has an important guiding significance to the stable processing of the machine tool. The traditional methods mainly involve a mounted sensor or direct embedment into the structure and hybrid finite element methods (FEMs), which are inconvenient. The FEM accuracy is limited by the accuracy of the model. This paper reports that feed-drive system vibration is excited by the interactive impact of a screw, ball, and nut, whose interaction is the core of the feed-drive system dynamics. Under the action of current, the ball screw serves as the transmission component of the executive component to control the working condition of the sliding table. This paper proposes a new method that uses the inertia force sequence caused by random idle running of the sliding table to identify the natural frequency of a feed-drive system based on the feed motor current response. First, the corresponding relationship between the current and the vibration of the feed-drive system is studied. The accuracy of the new method based on the current response is analysed by the experiment, which is performed to identify the natural frequency of the feed-drive system. Then, the incentive region is changed to study the identification effect under different interactive impact conditions, where the contact condition of the ball screw is different. [ABSTRACT FROM AUTHOR]

A series of experiments are conducted on a 252-kV pantograph disconnector offline to monitor its mechanical condition. Three parameters, namely the dynamic strain of the post insulator, the rotation angle of the operating shaft, and the stator current of the driving motor, are measured by resistance strain gauges, magnetic-sensing angle transducer, and Hall current sensor, respectively. Measurement results of the three parameters under mechanical fault conditions including clamping stagnation of the conduct tube knuckle, failure of the balance spring, and uncompleted closing operation are compared with those in normal operating condition of the disconnector. The results indicate that the dynamic strain waveform, the rotation angle curve, and the motor current envelope waveform may get distorted with increased peak value and offset peak time when mechanical faults happen to the transmission or rotating mechanism of the disconnector, and more component of the side frequency will appear in the spectrum of the motor current waveform. The proposed methods are validated to be effective for mechanical fault diagnosis and condition assessment of pantograph disconnector, and are also of reference significance for other types of disconnectors. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The condition of a cutting tool is an important factor in any metal cutting process. Several different methods have been developed to monitor the condition of tools in turning. Motor current measurement is one of the indirect tool wear monitoring methods in machining processes. The advantage of motor current measurement method over other methods for detecting malfunctions in the cutting process is that the measuring system has an acceptable cost and the measurement apparatus does not disturb the machining process. In this paper, the effect of tool wear and tool breakage on the harmonics of the AC spindle motor is investigated. Finally, a method based on time-frequency marginal integral of current signal is proposed for indirect measurement of tool wear in turning. In order to validate the proposed tool monitoring system, an accelerometer was attached to the shank of the cutting tool to measure vibrations resulting from tool wear in feed direction. A vision system was also used to measure tool wear and also traces tool state by observing the surface texture of the workpiece. Performance of the proposed system was validated against different experiments. The results showed improved robustness of the proposed system with high potential for practical application in detecting tool wear. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) is a solid-state welding process that is ideally suitable for joining materials with low melting point, such as aluminium alloys. In this paper, development of a fuzzy logic model to monitor weld strength and nugget hardness of friction stir butt-welded AA6063-T6 aluminium alloy plates is presented. FSW experiments were carried out with different process parameters in vertical spindle milling machine. The generated acoustic emission (AE) and machine spindle motor current during the FSW process were acquired with the aid of data acquisition system. A fuzzy logic model based on AE signal and motor current was developed to predict the weld strength and nugget hardness. The fuzzy model accuracy was validated with experimental data. The proposed model could be used for online weld monitoring of FSW process within a range of process parameters. [ABSTRACT FROM AUTHOR]

The algorithm for the physiological control provided by left ventricular assist devices (LVADs) has been controversial. In particular, little is known about the physiological control algorithm (such as for achieving physiological circadian rhythms) in continuous-flow LVADs. To investigate the existence of circadian variation, we retrospectively evaluated the LVAD flow-correlated motor current of patients supported by continuous-flow LVADs. The motor current and the pump speed were collected from the external controller every 10 min after device implantation, and the data were divided for every 30-day period, which began on midnight on the first post-operative day. The subjects were 18 patients (mean age 37.7, mean body surface area 1.71 m at the time of operation) with dilated cardiomyopathy or dilated phase of hypertrophic cardiomyopathy. As of August 1, 2013, the patients' median support duration was 889 days. The mean calculated dominant period of motor current variation was 24.0 h and the mean amplitude was 11.7 mA for the entire duration. The amplitude of the motor current circadian variation tended to be increased until around the fifth month. The motor current had a tendency to be relatively low during the night time and high during the day time. A significant difference was found between the night-time and day-time mean motor current for the entire duration ( p < 0.05). In conclusion, the circadian variation of the motor current could be observed over long term in patients with fixed rotation speed continuous-flow LVAD support. [ABSTRACT FROM AUTHOR]

Brushless DC (BLDC) motor fan is one of the most widely used electronic components for thermal management. Failures of fans will cause seriously problems, such as overheat, shut down and even burnt, in their host systems. Thus, anomaly detection of fan gains increasing attention. This paper investigates into anomaly detection of generalized-roughness bearing faults in BLDC motor fan using current monitoring technique. In the experiment, it was found that there were about 9% changes in current and rotary speed of fan when the BLDC motor behaved unusual. Mathematical modeling of BLDC motor and physics-of-failure analysis were employed to explain the experimental findings. [ABSTRACT FROM AUTHOR]

This paper describes an extrapolation scheme for high-resolution rotor-position prediction using data from a low- resolution encoder. The predictor approximates the previous rotor movement profile by a polynomial equation, which is then used to predict the subsequent rotor motion. The low- and transient-speed characteristics are examined, and it is shown experimentally that the technique can produce effective resolution improvements by a factor of 9. When the rotor velocity reverses, large errors can arise, but it is demonstrated that this problem can be overcome by including motor current data in the position predictor. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a tool condition monitoring system (TCMS) for on-line tool wear monitoring in turning. The proposed TCMS was developed taking into account the necessary trade-off between cost and performance to be applicable in practice, in addition to a high success rate. The monitoring signals were the feed motor current and the sound signal. The former was used to estimate the feed cutting force using the least squares version of support vector machines (LS-SVM). Singular spectrum analysis (SSA) was used to extract information correlated with tool wear from the sound signal. The estimated feed cutting force and the SSA decomposition of the sound signal alone with the cutting conditions constitute the input data to the TCMS. Again LS-SVM was used to estimate tool condition and its reliability for on-line implementation was validated by experiments using AISI 1040 steel. The results showed that the proposed TCMS is fast and reliable for tool condition monitoring. [Copyright &y& Elsevier]

In recent years, a number of machining centers have been built using linear motors. These machining centers have great potential for precision and high-speed machining. Nevertheless, a number of problems remain unsolved, such as monitoring and control. This paper presents a new tool breakage monitoring method for this type of machining center using the current signal of the linear motor. First, the relationship between the cutting force and the motor current is analyzed. Then, the new tool breakage method is presented. From a mathematical point of view, the new method uses a nonlinear energy operator to capture the abrupt changes of the motor current signal, which is directly related to the tool breakage. The experiment validation is included. [ABSTRACT FROM AUTHOR]

Control of the ventricular assist device (VAD) for native heart preservation should be attempted, and the VAD could be one strategy for dealing with the shortage of donors in the future. In the application of nonpulsatile blood pumps for ventricular assistance from the ventricular apex to the aorta, bypass flow and hence the motor current of the pumps change in response to the ventricular pressure change. Utilizing these intrinsic characteristics of the continuous-flow pumps, in this study we investigated whether motor current could be used as an index for continuous monitoring of native cardiac function. In study 1, a centrifugal blood pump (CFP) VAD was installed between the apex and descending aorta of a mock circulatory loop. In this model, a baseline with a preload of 10 mmHg, afterload of 40 mmHg, and LV systolic pressure of 40 mmHg was used. The pump speed was fixed at 1300, 1500, and 1700 rpm, and LV systolic pressure was increased up to 140 mmHg by steps of 20 mmHg while the changes in LV pressure, motor current, pump flow, and aortic pressure were observed. In study 2, an in vivo experiment was performed using three sheep. A left heart bypass model was created using a centrifugal pump from the ventricular apex to the descending aorta. The LVP was varied through administration of dopamine while the changes in LV pressure, pump flow, and motor current at 1500 and 1700 rpm were observed. An excellent correlation was observed in both in vitro and in vivo studies in the relationship between motor current and LV pressure. In study 1, the correlation coefficients were 0.77, 0.92, and 0.99 for 1300, 1500, and 1700 rpm, respectively. In study 2, they were 0.88 (animal no. 1), 0.83 (animal no. 2), and 0.88 (animal no. 3) for 1500 rpm, and 0.95 (animal no. 2) and 0.93 (animal no. 3) for 1700 rpm. These results suggest that motor current amplitude monitoring could be useful as an index for the control of VAD for native heart preservation. [ABSTRACT FROM AUTHOR]

Abstract: The control strategy for ventricular support with a centrifugal blood pump was examined in this study. The control parameter was the pump rpm that determines pump flow. Optimum control of pump rpm that reflects the body's demand is important for long-term, effective, and safe circulatory support. Moreover, continuous, reliable monitoring of ventricular function will help successfully wean the patients from the ventricular assist device (VAD). The control strategy in this study includes determination of the target pump rpm that can provide the flow required by the body, fine-rpm-tuning to minimize deleterious effects such as suction in the ventricle, and assessment of ventricular function for successful weaning from VADs. To determine the target pump rpm, we proposed to use the relation between the native heart rate and cardiac output, and the relation between the pump rpm and centrifugal pump output. For fine-tuning of the pump rpm, the motor current waveform was used. We computed the power spectral density of the motor current waveform and calculated the ratio of the fundamental to the higher order components. When this ratio was larger than approximately 0.2, we assumed there would be a suction effect in the ventricle. As for assessment of ventricular function, we used the amplitude of the motor current waveform. The control system implemented using a DSP functioned properly in the mock circulatory loop as well as in acute animal experiments. The motor current also showed a good correlation with the ventricular pressure in acute animal experiments. [ABSTRACT FROM AUTHOR]