Sensor Fault Diagnosis Method Research Articles

Study of the fault diagnosis method for gas turbine sensors based on inter-parameter coupling information

Correct and reliable measurement data are crucial for state monitoring, safe operations, health assessment, and life prediction of integrated energy systems (IESs). Sensors are often installed in harsh environments and prone to all kinds of faults; therefore, it is necessary to diagnose sensor faults. A diagnostic method for sensor faults based on gradient histogram distribution (GHD) combined with light gradient boosting machine (LightGBM) is presented in this paper. This proposed method effectively utilizes the coupling information between the relevant parameters. The GHD efficiently extracted the time-domain characteristics of sensor faults and reduced the dimension of eigenvectors. This is beneficial to increasing the diagnostic speed. The kernel density estimation distributions of the gradient and eigenvectors for the sensor with strong correlation are similar, but that for the sensor with weak correlation are completely different. A LightGBM classifier trained based on the feature vectors was utilized to diagnose and classify the sensor faults. The diagnosis accuracy and the diagnosis time of this developed method were examined using the multiple-condition practical operation data of gas turbines in the IES. The experiment results demonstrate that the diagnostic accuracy of five sensor faults using this developed method is all above 90%. The diagnostic time is about 0.47–1.34 s, and is less than 2 s for the gradual faults.

Voltage and Current Sensor Fault Diagnosis Method for Traction Rectifier in High-Speed Trains

The traction rectifier plays a key role in high-speed trains. Unexpected failure often occurs in the sensors of the rectifier, which may affect the control performance of the electric traction rectifier and even cause serious deterioration to high-speed trains. A sensor fault diagnosis method is presented in this paper, considering three kinds of common fault types. It can not only locate the sensor fault, but also identify fault types. Based on the influences of the sensor faults, the fault diagnosis thresholds can be calculated quantitatively. No additional hardware is required. First, the model of the rectifier is established, and the estimator is built. The current residuals with different faults can be obtained. Next, residuals are analyzed and features are acquired. Then, diagnosis functions are constructed, which are used for fault location and fault type identification. Finally, the feasibility and effectiveness of the method have been confirmed by the experimental results.

Attention-empowered transfer learning method for HVAC sensor fault diagnosis in dynamic building environments

The Heating, Ventilation and Air Conditioning (HVAC) system is a key system in buildings for providing energy-saving and occupant-centered indoor services. Sensor fault diagnosis (SFD) is essentially important for HVAC systems since incorrect sensory measurements may destabilize system operations. Most SFD methods for HVAC systems require comprehensive manual knowledge or massive labeled data which is hardly available in diverse buildings. To reduce labor cost, transfer learning method is adopted in diagnosing HVAC sensor faults, however, it still encounters critical challenges, such as imbalanced data distribution and insignificant fault features. In this paper, an attention-empowered transfer learning method is innovatively proposed to enhance the capability of SFD in HVAC systems. Firstly, we introduce the multi attention-based module to characterize sensor faults in spatio-temporal domain. Secondly, we explore the relationship between source domain and target domain, and establish the model that enables useful knowledge mapping. Thirdly, to alleviate the effect of data discrepancy existed in new domains, a joint domain loss function is designed to enhance the domain adaptive ability. Experimental validation is carried out on three datasets from completely different real-world scenarios. Compared with existing methods (i.e., TCA, JAN and DDC), the proposed method achieves the highest average accuracy of 93 % in cross-domain building environments. The ablation studies also demonstrate the effectiveness of our method under diverse parameter settings, such as attention modules, fault types and data volume.

Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost

Vibration sensors are prone to bias, drift, and other failures. To avoid misjudgments in state monitoring systems and potential safety accidents caused by vibration sensor failures, it is significant to diagnose the faults of vibration sensors. Existing methods for vibration sensor fault diagnosis are primarily based on Deep Learning, but Extreme Gradient Boosting stands out due to its excellent interpretability, and compared to other ensemble learning algorithms, it boasts superior accuracy and efficiency. Therefore, a vibration sensor fault diagnosis method based on Extreme Gradient Boosting is proposed to diagnose seven common types of faults in vibration sensors. To prevent the model from being overwhelmed by simple negative cases during training, a new loss function named Triage Loss is designed to improve the classifier’s performance. The vibration sensor fault diagnosis has confirmed the efficacy and practicality of the suggested approach. The experimental results indicate that the training of the model done using Triage Loss outperforms the training model done using the default loss function, with a maximum improvement of 5.4% accuracy, 5.45% in the F1-score, and 9.87% in the mean Average Precision under different fault rates.

A Novel Set-Valued Sensor Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

Sensor fault diagnosis is of great significance to ensure safe battery operation. This paper proposes a novel sensor fault diagnosis method that achieves the simultaneous fault detection, fault source and type identification, and fault estimation in a comprehensive way. Specifically, a set-valued observer, featuring a state predictor and a state estimator, is first constructed and designed to guarantee the inclusion of the unavailable actual battery state due to unknown modeling errors and noises at every instant of time. Compared with the traditional observers, a distinct feature of the proposed one lies in that the calculated state predictions and estimations of the battery system at each time step are ellipsoidal sets in state space rather than single vectors. The boundedness of state prediction and estimation errors is formally proved, and the tractable design criteria for determining the real-time optimal prediction and estimation ellipsoids are also derived. As for diagnosis algorithm, fault detection is implemented based on the intersection between the prediction and estimation ellipsoids. Then, a two-layer Pearson correlation coefficient analysis mechanism is developed to identify the source and type of sensor faults. Another set-valued observer based on an augmented battery model is further designed to estimate the fault level. Finally, experimental studies of a battery cell under different sensor fault sources, types and values are elaborated to verify the effectiveness of the proposed method.

Data-Driven Sensor Fault Diagnosis Under Closed-Loop Control With Slow Feature Analysis

Recently, data-driven fault diagnosis techniques, especially multivariate statistical process monitoring methods, have been extensively investigated and widely applied to industries. Whether the monitored system is under closed- or open-loop control has a strong impact on the development of fault diagnosis strategies, but this point has not been taken seriously into consideration. This work aims to provide an effective data-driven method for sensor fault diagnosis under closed-loop control with modified slow feature analysis (SFA). The SFA is first revisited and compared with the well-known principal component analysis approach. Through theoretical analysis and an intuitive example, the influence of feedback control on sensor fault diagnosis is demonstrated, and to achieve successful detection, a strategy of incorporating more variables for monitoring is suggested. Then, based on SFA, a new sensor fault detection and classification method is developed. Its detectability analysis is carried out, based on which improved methods are proposed for efficient detection of incipient sensor faults. Finally, case studies on the continuous stirred tank reactor benchmark process demonstrate the effectiveness of the proposed method.

Rapid Sensor Fault Diagnosis for a Class of Nonlinear Systems via Deterministic Learning.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

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.

A novel H∞ observer for sensor fault diagnosis of vehicle electronic stability control system with disturbance and input delay

This paper mainly focuses on the sensor fault diagnosis method based on H∞ observer in vehicle electronic stability control (ESC) system with disturbance and input delay. Firstly, the four-wheel steering vehicle model is established. Secondly, the system state and sensor fault are reconstructed into new system variables by a special matrix transformation method, based on which a new H∞ observer is developed to estimate system state and sensor fault signals. Then, based on Lyapunov method and linear matrix inequality (LMI) theory, the conditions for asymptotic stability and H∞ performance of the estimation error system are proved. Finally, Carsim and Matlab/Simulink co-simulation are used to show the effectiveness of the method.

Deep residual neural-network-based robot joint fault diagnosis method

A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

A hybrid clustering multi-source fault diagnosis method for chiller temperature sensors

ABSTRACT Sensor faults have been observed to negatively impact the operation of the HVAC system. Among these faults is the complexity of multi-source sensor faults, which may result in fault confusion due to multiple fault points and different fault patterns. This paper proposes a fault diagnosis model applicable to single- and multi-source faults of HVAC system sensors. Based on the distribution patterns of chillers sensor data, the ensemble empirical mode decomposition soft threshold denoising Gaussian mixture model (EEMDSTD-GMM) is proposed. The study suggests a K-means-based pre-classification method for potentially confusing types of sensor faults. EEMDSTD-GMM-K-means has shown a better fault diagnosis capability under four single-source sensor faults and five multi-source sensor faults. Under the three examined fault levels, the results indicate a satisfactory performance with an average diagnosis rate of 98.7% for single-source faults and 96.5% for multi-source faults.

A novel improved data-temporal attention network reconstruction model-based method for fault diagnosis of chiller sensors

Accurate measurement of sensors is the basis for the reliable operation of complex systems. To improve the fault diagnosis performance of the air-cooled chiller sensor, an improved data-temporal attention network is proposed, and an improved data-temporal attention network–based method for sensor fault diagnosis is established. First, the method combines the “memory” capability of the bidirectional improved long short-term memory network and the feature extraction capability of the multi-scale convolutional neural network to fully explore the time correlation of the chiller sensor, the data correlation between the physical quantities, and the dynamic response characteristics of the physical quantities. In addition, data and time attention mechanisms are introduced into the encoder and decoder, respectively, and valuable information is enhanced through weight distribution to capture relevant features further. Second, relying on the advantages of the “end-to-end” network structure of the improved data-temporal attention network model, the fault sensor is directly located by comparing the absolute reconstruction error vector with the fault threshold vector. Finally, it is verified by the datasets collected from a real air-cooled chiller platform that the proposed method has achieved an excellent fault diagnosis effect. The ablation experiment confirms that each improvement step can effectively improve the reconstruction performance of the network, which enhances the sensitivity of fault sensor identification. Compared with existing mainstream methods, the state-of-the-art framework presented in this article reveals its superiority.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

A current sensor fault diagnosis method based on phase angle shift technique applying to induction motor drive

<div id="eJOY__extension_root" class="eJOY__extension_root_class" style="all: unset;">An improved method using the phase angle shift characteristic of the sine wave is proposed to diagnose the fault states of the current sensors in an induction motor drive. The induction motor drive (IMD) system applied in this study uses the field-oriented control (FOC) loop with integrated two current sensors and a speed encoder to control the rotor speed. The space vectors created from the phase angle shift technique are compared to the estimated current for the fault diagnosis algorithm. Various types of current sensor failures are investigated by MATLAB/Simulink software to check the effectiveness of the proposed method. The simulation results have proved the performance of the proposed method in enhancing the reliability and stability of the IMD system.</div>

An FCS-MPC-based open-circuit and current sensor fault diagnosis method for traction inverters with two current sensors

Effective fault diagnosis methods can improve the reliability of the two-level inverter used in traction systems. Both the open-circuit (OC) fault and current sensor fault can affect the phase currents of the inverter, which makes it difficult to distinguish them. In this paper, a diagnosis method for OC faults and current sensor faults is proposed, which is based on finite control set-model predictive control (FCS-MPC). First, the cost function is constructed and the inverter is controlled by the FCS-MPC. Then, the actual and estimated values of the cost function are calculated. The difference between them is applied to fault detection. Next, Combine the command signals and current direction, the number of affected sampling points in a period is counted and normalized. Different faults correspond to different percentages and this feature is used to determine fault location. Finally, the hardware-in-the-loop (HIL) platform is built, the feasibility and effectiveness of the proposed fault diagnosis method are verified by the HIL tests.

A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack

Considering the limitations in existing correlation coefficient-based, entropy-based and big data analysis-based voltage sensor fault diagnosis methods, we develop a novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack in this paper. Firstly, a “ohmic resistance”-based selection method is periodically performed to artificially divide all in-pack cells into “representative cell” and non-representative cells. Secondly, during the “representative cell”-based battery pack state-of-charge (SOC) and cell SOC inconsistence estimation process, the measurement innovation (MI) between measured and estimated voltage of the “representative cell” and non-representative cells is generated in micro time-scale and macro time-scale, respectively. Regarding the “representative cell”, the faulty voltage sensor is immediately detected at the moment of the voltage sensor fault occurrence by catching the abnormal MI. As for the non-representative cells, through analyzing the discreteness degree of generated MI under faultless and faulty voltage sensors, an abnormal variance-based voltage sensor fault diagnosis method and an abnormal variance contribution-based fault location method are developed. The validation results through three sophisticated cases demonstrate that this method can rapidly catch the abnormal features for further voltage sensor fault diagnosis with low complexity and satisfactory robustness even though there exist certain faulty current.

Application of three principal component analysis techniques in sensor faults detection

The sensor is the front-end component of the bridge structural health monitoring system. In this study, three fault diagnosis methods based on principal element analysis are applied to a bridge health monitoring system. The diagnostic results show that significant differences among the operating conditions will deteriorate the robustness of the diagnostic methods. The applicability of the sensor fault diagnosis method is verified based on the principal element analysis of a practical health monitoring system.

Voltage and Current Sensor Fault Diagnosis Method for Traction Converter with Two Stator Current Sensors.

The traction converter is one of the key components of high-speed trains. Current and voltage sensor faults in the converter may lead to feedback values deviation and system degradation, which will bring security risks to the train. This paper proposes a real-time fault diagnosis method for grid current, DC-link voltage and stator current sensor faults in the traction converter with two stator current sensors, which can not only detect and locate faults but also identify the types of faults. Moreover, the faults considered in this paper are incipient. First, the DC-link model is established, and the fault is detected by the residual of the DC-link voltage. Next, the differential of DC-link voltage residual is calculated, which is applied to fault location. Then, according to the change of the differential values, different fault types are determined. Finally, the hardware-in-the-loop (HIL) platform is built and the effectiveness and accuracy of the proposed method are verified by the HIL tests.

A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier

Gas monitoring sensor is prone to failure and its fault type is difficult to identify due to harsh working condition. In this work, a new sensor fault diagnosis method for gas leakage monitoring has been proposed derived from the Naive Bayes Classifier (NBC) and Probabilistic Neural Network (PNN). Firstly, NBC is used to identify the abnormal safety monitoring data. Then PNN is employed for sensor fault classification. The feasibility and effectiveness of this method are verified by applying it to the urban gas pipeline leakage monitoring system. It is shown that the abnormal monitoring data can be online distinguished, and sensor fault type can be effectively recognized. The global accuracy of abnormal data identification and the global accuracy of sensor fault diagnosis can reach 85% and 95%, respectively. This work can provide a guideline to improve the reliability of the urban gas pipeline monitoring systems.

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.