Current Sensor Fault Detection Research Articles

Current Sensor Fault Detection and Fault-tolerant Control of Bearingless Induction Motor Based on VCS

Current Sensor Fault Detection and Fault-tolerant Control of Bearingless Induction Motor Based on VCS

Current Sensor Fault Detection and Identification for PMSM Drives Using Multi-Channel Global Maximum Pooling CNN

Current Sensor Fault Detection and Identification for PMSM Drives Using Multi-Channel Global Maximum Pooling CNN

Real-time current sensor fault detection and localization in DFIG wind turbine systems

<span lang="EN-US">The degree of reliability is one of the main issues in having the uninterrupted operation of fault-tolerant measurement and control systems. This paper presents a technique for detecting and localizing current sensor fault in doubly fed induction generator wind turbine systems (DFIG-WT). For this purpose, a methodology divided into three steps has been carried out and is presented as follows: the first step is the application of the zero-sequence component as an indicator of the presence of the sensor fault. The second step is based on the application of Concordia transformation on the current signals to generate different criteria for localizing the faulty sensor. Finally, an artificial neural network model is developed to analyze the localization criteria and identify the faulty sensor. All simulations are carried out in MATLAB/Simulink environment. Results show that the proposed technique is effective and can be used in real-time fault detection and localization in the DFIG-WT.</span>

A hybrid multiple sensor fault detection, diagnosis and reconstruction algorithm for chiller plants

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented.

Sensor Fault Diagnosis Method Based on Rotor Slip Applied to Induction Motor Drive.

A novel diagnosis method based on the rotor slip is proposed in the paper to correctly detect current and speed sensor failures during the induction motor drive (IMD) operation. In order to enhance reliability and avoid confusion in the diagnosis algorithm due to the influence of measured signal quality, each fault type is determined in a priority order defined by the diagnosis method. Based on the features of the IMD applying the field-oriented control (FOC) technique, an innovative model uses the measured currents and reference speed as the input signals to estimate the rotor slip for the current sensor fault detection. After verifying the quality of the feedback of the current signals, a speed sensor fault function is continued, and performs according to relations among the reference speed, estimated speed based on the sliding mode method, and measured rotor speeds. Finally, the estimated quantities are selected to replace the wrong measured current or speed signals. The feasibility of the proposed approach is verified by simulations using Matlab-Simulink software as well as by practical experiments using an IMD prototype with a rated power of 2.2 kW and a DSC-TMS320F28335-based control system. The obtained simulation and experimental results demonstrated the feasibility, effectiveness, and reliability of the proposed diagnosis technique in detecting sensor failures and maintaining the stable operation of the IMD.

Single current sensor based fault tolerant control of interior permanent magnet synchronous machine for drive applications

This paper presents an integrated method for current sensor fault detection and fault tolerant control (FTC) for traction interior permanent magnet synchronous motor (IPMSM). The proposed current sensor fault detection method is based on detecting changes in the d-q axis current. The FTC is based on d-q axis current estimation from the reference d-q axis current and the phase current measured by the surviving current sensor. The current estimation process is independent of machine parameters. Hence the estimation is robust and requires less computational cost. The effectiveness of the FTC method is verified by the transient analysis. Such FTC is suitable for electric vehicle traction applications to ensure non-stop control operation of the drive in the entire range of speed. The efficacy of the proposed FTC method is tested through extensive simulations in MATLAB/Simulink environment. The real-time applicability of the proposed FTC method using the cost-effective digital signal processor (DSP) is verified on Texas Instruments© TMSF28379D through the processor in loop (PIL) simulation model.

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Cri Markers in the PMSM Drive System

In this paper the current sensor fault detector for the permanent-magnet synchronous motor drive system has been presented. The solution is a known method used for induction motor drive systems, tested by authors in simulation for the PMSM drive system. The application is based on the current markers, which enable not only failure detection, but also the location of said failures. Detector operation is based only on the analysis of measurements from current sensors and does not require additional information about other state variables. The aim of the work is to present simulation and experimental studies in field-oriented control (FOC) for the tested current sensor fault detector for various operating conditions of the drive system—variable speed and load.

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.

Universal System for Detection and Compensation of Current Sensor Faults in Three-Phase Power Electronic Systems

Abstract The article discusses the universal current sensor fault detection and compensation mechanism, which can be applied in three-phase power electronics (PE) symmetrical system. The mechanism is based on the assumption that a symmetrical system can be described using different components in the stationary reference frame. The solution given in article as a Cri-base detector was tested in electrical drives with induction motors (IMs) and permanent magnet synchronous motors (PMSMs). This study also proves that the same algorithm can work stable in active rectifier systems. Such an application of this detector has not been previously reported in the literature. The article describes the detection of various types of faults in different phases. The fault-tolerant voltage-oriented control (FTVOC) of an active rectifier is compared with previously described solutions for IMs and PMSMs. By analysing in various types of systems, the work proves the universality of the detector based on Cri markers.

Sensorless FOC Strategy for Current Sensor Faults in Three-Phase Induction Motor Drives

Current sensors are required in Field-Oriented Control (FOC) strategies of Three-Phase Induction Motor (TPIM) drives. Nevertheless, the current sensors are subject to different electrical/mechanical faults which reduce the safety and dependability of the drive system. Single phase current sensor Fault-Tolerant Control (FTC) for sensorless TPIM drives using flux observer and Extended Kalman Filter (EKF) is proposed in this research. In the suggested FTC scheme, current sensor fault detection is based on axes transformation, a logic circuit is served as the fault isolation and reconstruction of faulted currents are achieved through flux observer and EKF. The presented FTC system is capable of detecting and localizing the current sensor fault and switching the drive system to tolerant FOC mode without the rotor speed measurement. The effectiveness of the suggested FTC system is confirmed by experiments on a 0.75kW TPIM drive platform.

Current Sensor Fault Detection and Isolation in Doubly Fed Induction Generator

Fault Detection and Isolation (FDI) is gaining interest as a means to increase the reliability and availability of efficient systems. Measuring sensors used for data acquisition are more prone to failures. The main objective of this paper is to provide model based FDI for current sensor fault closed-loop controlled Doubly Fed Induction Generator (DFIG) model connected to power grid. The sensor fault was detected and isolated by two methods including Unknown Input Observer (UIO) on reduced order DFIG model and fuzzy logic. The results show improvement in computational speed during online monitoring when the fault isolation is performed on reduced order models.

A Sign Logic-Based Method of Current Sensor Fault Detection for PMSM Drivers

A simplified method of current sensor fault detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) drives, which only requires to collect the information of motor positions and currents of the measured phases, has been proposed in this paper. Compared with the classical state-observer-based approaches, the calculations needed in the new method only involve a few addition and logical operations. The simplicity and reliability of the new method makes itself especially useful for fault detection of current sensors in real-time control systems with limited computational capability, e.g., single-chip microcomputers (SCM) or field programmable gate arrays (FPGA). The experimental results on the basis of a FPGA controller have validated the feasibility and robustness of the proposed FDI approach.

A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

Simple and Robust Current Sensor Fault Detection and Compensation Method for 3-Phase Inverters

The present paper proposes a genuine, simple to implement, robust and reliable method for current sensors fault detection and compensation used for 3-phase inverters. Its functionality is based on an algorithm programed into a Field Programmable Gate Array (FPGA) as general controller. Usually, 3-phase inverters are controlled using field oriented control (FOC) which is generally triggered to sample the measured currents on the established switching frequency. As the latter is much smaller than the base clock of the FPGA, this allows hundreds of free time samples to perform other calculations in-between two FOC samplings. In this paper, a method that uses these free and unused time samples is presented. This method performs calculations for fault detection and compensation ensuring that at each new FOC sampling, this will receive the correct current data to reach continuous operation despite faults. The interleaving principle of the FOC with the fault detection method, as it will be proven in the paper, ensures high reliability of the complete controller diminishing the possibility of undesired or faulted readings to disturb the FOC’s calculations. The fault detection philosophy is based on continuous comparison of the instantaneous measured currents (sensor’s response) against reference values. The experimental results presented in the paper prove reliable operational performances of the method in both steady state and transient conditions. The added value of the paper consists in its genuine approach to handle the fault detection and compensation in-between two PWM ticks, ensuring that no faulted measurements can reach the control unit. This added value is based on functionality divided on several functions triggered by an internal generated clock synchronizing and handshaking their operations towards one goal: fault detection and compensation.

Enhanced Control Algorithm in PMSMs with Machine Current Sensor FDI and Current Management for Hardware Protection

This paper proposes an enhanced control algorithm in permanent magnet synchronous machines (PMSMs) with machine current sensor fault detection and isolation (FDI) and current management for hardware protection. Different from existing work on machine current sensor FDI, the proposed method addresses the sensor FDI problem under co-existence of PMSM current sensor and non-sensor imbalance faults, and thereby help prevent the controller performance degradation caused by a faulty machine current sensor signal. Meanwhile, hardware protection is achieved through the proposed algorithm which has a unique feature of providing online overcurrent regulation and source power management at the same time to enhance protection of power inverter and power source. Benchmark comparison of the proposed sensor FDI method to state-of-the-art techniques is carried out to validate the good performance of the proposed FDI method in terms of fault detection time, false detection rate and missed detection rate. Sensitivity analysis of the proposed current management algorithm with overcurrent regulation and source power management is performed under changing rotor flux linkage and machine stator d-axis self-inductance, as an example to demonstrate the robustness of the proposed algorithm for hardware protection against system parameter variations. The effectiveness of the proposed algorithm is confirmed by simulation results in MATLAB for a PMSM.

Machine Current Sensor FDI Strategy in PMSMs

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor fault and non-sensor fault. The proposed method is validated with simulation studies in MATLAB, and the studies demonstrate that the proposed machine current sensor FDI method, different from existing methods, has a unique feature of being able to handle multiple sensor faults with the consideration of non-sensor fault disturbance.

Two current sensor fault detection and isolation schemes for induction motor drives using algebraic estimation approach

The paper focuses on single and multiple current sensors fault detection and isolation (FDI) for induction motor (IM) drives using only two current sensors. Two model based diagnosis methods are proposed to estimate the current residuals dynamics in the stationary reference frame: The first one is a differential algebraic estimation of fault dynamics and the second one is based on a combination of the Robust Integral Sign of the Error (RISE) observer with the algebraic approach. After detecting the fault occurrence, a logic based decision unit is then built to identify the faulty sensors. The resulting residuals are robust to load torque disturbances. Moreover, the developed fault estimators are simple with a 1-D model, based only on the input–output measurements and their derivatives and do not require a bank of observers. Furthermore, the used residual threshold is well defined and is suitable for the whole operating range. The ability of diagnosing the recovery of a sensor from a fault is also demonstrated. The validity of the developed approaches is analytically proved and a comparative study is carried out between the two FDI schemes.

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

Current sensor fault detection and isolation method for PMSM drives, using average normalised currents

A new approach for current sensor fault detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) drives is presented. Contrary to the classical approaches for sensors fault diagnosis, based on residual generation through observers or parity equations, the proposed technique uses the average normalised machine-phase currents. The main advantages of this approach are that it does not need any information about the PMSM or inverter models, and it involves low tuning efforts, that make it suitable for real-time implementation with good reliability. The good performance and the robustness of the proposed FDI approach are illustrated through experimental results.