Submodule Reference Voltage Similarity-Based Current Sensor Fault Diagnosis for N-Module PMSM Drive Systems

Permanent magnet synchronous motors (PMSMs) drives using three-phase voltage-source inverters (VSIs) are currently used in many industrial applications. The reliability of VSIs is one of the most important factors to improve the reliability and availability levels of the drive. Accordingly, this paper presents a robust fault diagnostic method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in three-phase PMSM drives. The proposed observer-based algorithm relies on an adaptive threshold for fault diagnosis. Current sensor and open-circuit faults can be distinguished and the faulty sensors and/or power semiconductors are effectively isolated. The proposed technique is robust to machine parameters and load variations. Several simulation and experimental results using a vector-controlled PMSM drive are presented, showing the diagnostic algorithm robustness against false alarms and its effectiveness in both IGBTs and current sensors fault diagnosis.

This study proposes an advanced current and position sensor fault diagnosis mechanism for permanent magnet synchronous motor (PMSM) applications. Machine nonlinear dynamics as well as parameter and load uncertainties are addressed. The features of the results of this study are summarized as follows: 1) a proportional-type full-state observer is combined with a disturbance observer to deal with the model uncertainties, 2) a partial optimal process is introduced for determining the observer gain, 3) the concept of normalized residuals is introduced for fault diagnosis criteria, and 4) a closed-loop analysis of the convergence, performance recovery, and offset-free guarantee is provided. The performance recovery property makes the proposed algorithm robust to parameter uncertainties and load variations, and sensitive only to sensor faults. In addition, the location and type of sensor faults can be identified by the fault diagnosis criteria using the normalized residuals. The performance of the sensor fault diagnosis algorithm was experimentally verified under several credible scenarios using a 7.5-kW PMSM.

This article proposes a fault diagnosis and fault-tolerant control method for three fault modes of current sensors in the doubly salient electromagnetic motor (DSEM) drive system, namely signal loss, gain variation, and zero offset. First, the fault features of current sensors under different fault modes are extracted based on the variation characteristics of three-phase currents in different working sectors to locate and distinguish the fault. Then, a fault-tolerant control method for current sensors is developed to reconstruct the fault phase current under the signal-loss fault and compensate the current online under the gain-variation and zero-offset faults, based on the remaining healthy phase currents and estimated fault coefficients. The proposed method only requires information of three-phase currents and the rotor position, instead of an accurate system model, and immunizes the variation of load and field current, having the advantages of simple implementation and superior robustness. Both simulation and experimental results verify the effectiveness of the proposed method.

Reliable fault diagnosis and identification are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. Since open-switch fault and output current sensor fault both affect the residual of output phase current, it is difficult to identify them apart from each other. Thus, this article proposes an open-switch fault and output current sensor fault diagnosis and identification method for the matrix converter (MC)-based PMSM drive system. The finite control set-model predictive control (FCS-MPC) method is applied in the MC-based PMSM drive system. The fault identification method is proposed based on the extracted different faulty features of the open-switch fault and the output current sensor fault. After the open-switch fault is separated from the current sensor fault, an error-voltage-based open-switch fault diagnosis strategy is applied to locate the faulty switch. Hardware-in-the-loop tests are carried out to verify the effectiveness of the proposed method.

A novel sensor fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition and Probabilistic Neural Network

Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal

The fault diagnosis of hydrogen sensors is of great significance. However, it is difficult to collect data samples for some modes of hydrogen sensor signals, so the data samples may be unbalanced, which can seriously affect the fault diagnosis results. In this paper, we present a novel convolutional neural network (CNN)-based deep convolutional generative adversarial network (DCG) method (DCG-CNN) for gas sensor fault diagnosis. First, we transform the 1D fault signals of the gas sensor into 2D gray images for end-to-end conversion with no signal data information loss. Second, we use the DCG to enrich the 2D gray images of small fault data samples, which results in balanced sensor fault datasets. Third, we use the CNN method to improve the accuracy of fault diagnosis. In order to understand the internal mechanism of the CNN, we further visualize the learned feature maps of fault data samples in each layer of the CNN and try to analyze the reasons for the method's high performance. The fault diagnosis accuracy of the DCG-CNN is shown to be higher than that of other traditional methods.

A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method

The working conditions of rolling bearings during the running change in real time. Aiming at the problem of fault diagnosis of rolling bearing under complex working conditions, a new fault diagnosis (VHDBN) method based on variation mode decomposition (VMD), Hilbert transform (HT) and deep belief network (DBN) is proposed in this paper. Firstly, the proposed fault diagnosis method performs the VMD decomposition for the time domain signal in order to obtain a series of intrinsic mode functions (IMFs), and Hilbert envelope spectrum is obtained by Hilbert transform. The Hilbert envelope spectrum is used to construct the feature matrix, which is used as an input of the DBN network in order to obtain a fault diagnosis model. In order to test and verify the effectiveness of the proposed fault diagnosis method, the experimental data of rolling bearings under variable load is used in here. The experimental results show that the VMD-Hilbert envelope spectrum can better reflect the fault characteristics than the time domain spectrum, and the proposed fault diagnosis method under variable load has higher recognition accuracy than other comparison methods.

Permanent Magnet Synchronous Motors (PMSMs) are becoming increasingly popular in traction applications. PMSMs have gained popularity due to their efficiency, compactness and excellent features related to control. This is mainly because of the permanent magnet placement in the rotor, which negates any additional rotor excitation, hence reducing energy losses considerably. This makes them more efficient compared to induction motors. PMSMs also have a high-power density, enabling them to provide more power relative to their size. These features make them highly feasible in electric vehicles and aerospace applications where size and weight are one of the critical aspects. With the possibility of precise and smooth torque control, they are well adapted to applications that have strict requirements for precise speed and position control. Also, PMSMs provide superior dynamic performance with low maintenance since they do not contain brushes and commutators; hence, they are supposed to have a longer life. But PMSMs have also some challenges, the most critical problem that usually faces PMSMs is fault detection, mainly bearing faults. The bearings in PMSMs are critical components, and any defect in those components creates serious mechanical and electrical problems for the motor. Due to its defective state, the bearing may impose the emergence of vibration, raised friction, and wear, leading to inefficiencies, increased heat generation, and, finally, motor failure. Thus, early detection of bearing faults is indispensable; prolonged operation with faulty bearings results in catastrophic damage to the motor, longer downtime, and more expensive repairs. Thus, the fault diagnosis at the inception stage has become very critical now. In this paper, a technique based on cyclization dominant frequency analysis for bearing fault detection using the phase current of a motor has been proposed. The results have shown that this method can be used for monitoring and detecting bearing faults without the use of any additional sensor.

This paper deals with the dSPACE DS1103 based implementation of a simplified fuzzy logic speed controller (Simplified FLSC) for vector controlled permanent magnet synchronous motor (PMSM) drives. The simplification of FLSC obtains minimization and adjustment of the membership function used by each fuzzy variable for the drive system. The mathematical model of PMSM drives has been simulated in the MATLAB environment using Simulink and Fuzzy Logic Toolbox to analyze the performance of the drive system. The simulated results are then validated with the experimental investigation. For experimentation, the FLSC, co-ordinate transformation and hysteresis current controller (HCC) have been implemented in MATLAB environment with Simulink, Fuzzy Logic Toolbox and Real-time interface. Meanwhile, the hardware implementation consisting of dSPACE digital signal processor, voltage source inverter, resolver-to-linear DC converter, current sensors and PMSM equipped with speed resolver. The PMSM drive system has been tested under various operating conditions and load disturbances.

In this article, a fault diagnosis method is proposed to detect current sensor faults for the studied primary permanent-magnet linear motor (PPMLM) traction system, in which only two current sensors are required, and the gain fault and zero-offset fault can be distinguished. In this proposed fault diagnosis method, synchronous currents are estimated by the PPMLM model. By comparing the estimated and measured synchronous currents, estimation errors are calculated, and ac components can be extracted by corresponding filters. In the synchronous frame, the zero-offset fault and the gain fault can be detected by the single-frequency and double-frequency components, respectively. Furthermore, the fault phase also can be located. Compared with conventional fault diagnosis methods, the proposed one can distinguish gain fault and zero-offset fault but only two current sensors are required. Simulation and experimental results verify the effectiveness of the proposed fault diagnosis method.

In this paper, a fault diagnosis method is presented in order to detection the inter-turn short circuit fault in permanent magnet synchronous motor (PMSM) based on the comparative analysis of two types of controllers. This study consists in the sliding mode control (SMC) of the (PMSM) and the two controllers (fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC) by taking account of the presence of inter-turn short circuit fault. This study aims to select the most robust controller against the stator faults, load torque variation and reversing rotation speed. Tests regarding these two controllers associated with the sliding mode control were carried on a PMSM 5KW under to different loads, speed and severity degrees of failures for a functioning in healthy and degraded state. The obtained simulation results confirm the superiority and robustness of AFLC-SMC (insensitive to the load torque and the rotation direction, faster response time, decrease the oscillations, and minimization of THD) compared to FLC-SMC.

A fault diagnosis method for small pressurized water reactors based on long short-term memory networks

In this paper, we regulate the speed of a surface mount Permanent Magnet Synchronous Motor (PMSM) with only using current sensors. We use a back-Electromotive Force (back-EMF) based sensorless speed control technique. We reduce the α-β model of the PMSM using singular perturbation theory, which reveals two algebraic expressions for the estimation of the back-EMF signals. We use these expressions to drive a Quadrature Phase Locked Loop (Q-PLL) that estimates rotor position and speed and also estimates the disturbance. The rotor position estimate is used for Park transformation while the speed and disturbance estimates are used in a feedback linearization law to regulate the speed. Our development of the controller only assumes knowledge of the nominal parameters of the PMSM. In addition, we assume the external load to be time-varying and bounded but otherwise unknown. Finally, results from simulation and experiment are shown to confirm robustness, and high performance of the output feedback system.