Sensor Fault Diagnosis Scheme Research Articles

Braking Sensor and Actuator Fault Diagnosis With Combined Model-Based and Data-Driven Pressure Estimation Methods

The braking system is significant for intelligent vehicles, which influences vehicle safety directly. However, under harsh working conditions, the inevitable health degradation and even failure of the braking system may cause severe safety issues. This paper proposes a novel braking actuator and sensor fault diagnosis scheme with combined model-based and data-driven pressure estimation methods. The model-based wheel cylinder pressure (WCP) estimator is established first based on the mathematical model of the hydraulic control unit (HCU) from the perspective of cause. The data-driven WCP estimator is then proposed based on the vehicle dynamics multivariate time series (MTS) model with the gated recurrent unit (GRU) from the perspective of effect. However, acquiring a large dataset is a practical challenge for real vehicle tests, so a novel data augmentation (DA) method called shifting is presented to enhance the model generalization ability. Next, fault detection, isolation, and identification are realized by comparing the threshold and the cumulative sum (CUSUM) of the residuals generated by the combined WCP estimation methods. The validation results show that the proposed data-driven method outperforms the traditional multilayer perceptron (MLP), long short-term memory (LSTM), and Transformer regarding accuracy and generalization. Vehicle tests simulating sensor and actuator faults validate the proposed fault diagnosis scheme.

Open-Switch and Current Sensor Fault Diagnosis Strategy for Matrix Converter-Based PMSM Drive System

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.

Real-Time Fault Diagnosis and Fault-Tolerant Control Strategy for Hall Sensors in Permanent Magnet Brushless DC Motor Drives

The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy.

A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

Current and Speed Sensor Fault Diagnosis Method Applied to Induction Motor Drive

The paper proposes a novel approach based on a current space vector derived from measured stator currents to diagnose speed and current sensor failures in the field-oriented control of induction motor drives. A comparison algorithm between the reference and measured rotor speed is used to detect the speed sensor faults. A counter is added to eliminate the influence of the encoder noise in the diagnosis method. In this approach, estimated quantities are not used in the proposed speed sensor fault diagnosis strategy, which increases the independence between the diagnosis stages in the fault-tolerant control (FTC) method. Moreover, in order to discriminate between the speed sensor faults and the current sensor faults, a new approach combining the current space vector and a delay function is proposed to reliably determine the current sensor failures. The MATLAB-Simulink software was used to verify the idea of the proposed method. Practical experiments with an induction motor drive controlled by DSP TMS320F28335 were performed to demonstrate the feasibility of this method in practice. The simulation and experimental results prove the effectiveness of the proposed diagnosis method for induction motor drives.

Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis Using Nonlinear Adaptive Estimation Methods

This paper presents the design, analysis, and real-time experimental evaluation results of a nonlinear sensor fault diagnosis scheme for quadrotor unmanned air vehicles (UAV). The objective is to detect, isolate, and estimate sensor bias faults in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equation. Two nonlinear diagnostic estimators are designed to provide structured residualsfor fault detection and isolation. Additionally, after the fault is detected and isolated, a nonlinear adaptive estimation scheme is employed for estimating the unknown fault magnitude.The proposed fault diagnosis scheme is capable of handling simultaneous faults in the accelerometer and gyroscope measurements. The effectiveness of the fault diagnosis method is demonstrated using an indoor real-time quadrotor UAV test environment.

Sensor fault diagnosis for lithium-ion battery packs based on thermal and electrical models

Sensor fault diagnosis is a crucial technology for the battery management system. In this work, a sensor fault diagnosis scheme is proposed for the battery pack using equivalent models and particle filters. The thermal and electrical models of the battery pack are developed based on the Thevenin equivalent circuit model and the radial equivalent thermal model. The model parameters are identified by the recursive least square algorithm. The particle filter estimates the temperature and voltage of the battery pack, which overcomes the problems of system noise and nonlinearity. The studentized residual method based on a sliding window is developed to eliminate the residual outliers caused by uncertainty. Monte Carlo simulation is used to calculate the specific values of distribution function, mean and standard deviation for different sequences. The critical value table of the absolute value method of studentized residuals is obtained by interpolating. The cumulative sum of residual log-likelihood ratio method based on sliding windows is developed to calculate residuals. By multiple residual assessments, faults of voltage, current, temperature sensors, and batteries are detected and isolated. Different fault cases are simulated to verify the proposed scheme. The results of the experiment and simulation results verify the effectiveness of the proposed sensor fault diagnosis scheme.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

A novel data-temporal attention network based strategy for fault diagnosis of chiller sensors

In the air-cooled chiller system, sensor fault diagnosis has great significance for ensuring normal operation. However, according to the operating mechanism of the chiller system, sensors’ readings exhibit dynamical data-temporal dependencies and are easily affected by external factors and control parameters. To further capture the data characteristics existed in the sensors time series, in this paper, we propose a novel data-temporal attention network (DAN) for the chiller sensor fault diagnosis. Based on the conventional encoder-decoder network (EDN), the proposed DAN model is firstly built by adding three novel parts: the first one is a data attention mechanism embedded in the encoder, which is used to capture the dynamic data correlation between different sensors; the second part is a temporal attention mechanism, which is employed in the decoder to model the dynamic time-dependencies among the sensors time series; considering the influence of external factors and control parameters, the third part is a fusion module to incorporate these influential factors from different domains. Thereafter, we design a specific chiller sensor fault diagnosis strategy using the proposed DAN model. The sensor fault diagnosis strategy uses only normal sequences for training and learns to reconstruct normal time series behaviors, and then determines the fault threshold of each chiller sensor, and finally identifies the specific sensor fault by comparing the absolute reconstruction error vector with the fault threshold vector. In the end, the experiments which adopt data sets from a real air-cooled chiller platform are conducted, and detailed comparisons are made. Various magnitudes of fixed biases are introduced into eleven sensors for validation. Experimental results reveal that the sensor fault diagnosis strategy with the proposed DAN model achieves the best training and fault diagnosis performance compared with its variants and the traditional EDN model. Especially for the sensor fault diagnosis performance, comparison results demonstrate that the proposed DAN model is more sensitive to the small biases than the other contrast models and has better robustness for impacts on fault sensor in the reconstruction of the fault-free sensors.

Distributed sensor fault diagnosis for a formation system with unknown constant time delays

In this paper, a distributed velocity sensor fault diagnosis scheme is presented for a formation of a second-order multi-agent system with unknown constant communication time delays. An existing distributed proportion-derivation (DPD) formation control law is adopted and a delay-independent condition is proposed to guarantee the asymptotical formation stability of the formation system based on the Nyquist stability criterion. Then a distributed fault diagnosis scheme is developed. In each agent, a distributed fault detection residual generator (DFDRG) and a bank of distributed fault isolation residual generators (DFIRGs) are designed based on the closed-loop model of the whole system. Each DFIRG is built up on the basis of a reduced-order unknown input observer (UIO) which is robust to the fault of one neighboring agent. According to the robust relationship between DFIRGs and faults, distributed fault isolation can be achieved. Conditions are presented to guarantee that each agent is able to diagnose faults of itself and its neighbors despite the disturbance of time delays. Finally, outdoor experimental results illustrate the effectiveness of the proposed schemes.

Distributed Design of Sensor Fault-Tolerant Control for Preserving Comfortable Indoor Conditions in Buildings

This paper proposes a distributed fault-tolerant control (FTC) scheme that can preserve thermal comfort conditions in a multi-zone building despite the presences of faulty temperature sensors. The proposed methodology exploits the networked structure of a Heating, Ventilation and Air-Conditioning (HVAC) system controlling the temperature of physically interconnected zones in order to design a distributed FTC control scheme comprised of a set of dedicated control agents. For each control agent, two adaptive bounds on the tracking error are derived, taking into account: (i) healthy sensor measurements and (ii) a single sensor fault. Each adaptive bound constitutes a condition that allows the selection of an appropriate local control gain such that the thermal comfort conditions are satisfied. By utilizing the decisions of a sensor fault diagnosis scheme, the controller gain can be reconfigured to compensate the effects of sensor faults. The proposed methodology is illustrated by simulating a sensor fault in a 3-zone HVAC system.

An Improved Sensor Fault Diagnosis Scheme Based on TA-LSSVM and ECOC-SVM

Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine (TA-LSSVM) method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine (ECOC-SVM) based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.

Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles

The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.

Structural Detectability Analysis of a Distributed Sensor Fault Diagnosis Scheme for a Class of Nonlinear Systems

The objective of this work is to analyze the performance of the local monitoring modules of a distributed diagnosis scheme tailored to detect multiple sensor faults in a class of nonlinear systems. The local modules monitor the healthy operation of subsets of sensors (local sensor sets). Every module is designed to detect the occurrence of faults in the local sensor sets when some analytical redundancy relations (ARRs) are violated. The set of ARRs is formulated using structured residuals and adaptive thresholds based on a nonlinear observer. In order to characterize the sensitivity of every monitoring module to local sensor faults, we obtain structural fault detectability conditions based on adaptive thresholds, and strong fault detectability conditions based on ultimate robust positively invariant sets. These conditions correspond to explicit relationships between the local sensor faults, the worst-case bounds on modeling uncertainties and the design parameters of the local monitoring module.

Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace

The paper shows a unified approach for designing both sensor and actuator fault diagnosis with neural networks. In particular, a general scheme of the group method of data handling neural networks is recalled. Subsequently, a unscented Kalman filter approach for designing the network and determining its uncertainty is briefly portrayed. The achieved results are then used to obtain the so-called robust sensor fault diagnosis scheme. The main contribution of this paper is to show how to use the above-mentioned results for actuator fault diagnosis. In particular, the obtained neural model is used to obtain the input estimates. The achieved estimates are then compared with the original input signals to formulate the diagnostics decisions. The input estimation scheme is based on a chain of robust observers, which guaranties that the input estimates are obtained with a prescribed disturbance attenuation level while ensuring the convergence of the observers. The final part of the paper shows a comprehensive case study regarding the laboratory tunnel furnace, which exhibits the performance of the proposed approach.