Fault Detection and Tolerant-Control Method for Sensorless Disposable Soft Manipulators

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

Fault detection based on robust characteristic dimensionality reduction

A Data-driven Fault Detection Method Based on Dissipative Trajectories

In this study, the use of instantaneous symmetrical components (ASB) for mechanical fault detection in an inverter controlled induction motor has been investigated. The proposed fault detection method is based on the calculation of the ASBs of the stator currents. The spectral density (PSD) of the positive sequence power component is estimated using the least squares method (EKY). Then, mechanical fault detection is performed using the generalized likelihood ratio test (GLRT). Modulated signals from both stator currents and closed-loop have been shown to be successful in detecting faults. The simulation results in the analytical model of an inverter controlled induction motor give the effectiveness of this proposed approach. This results in an effective fault detection method for an induction motor load torque oscillation.

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

This paper presents a simple fault detection method and fault-tolerant strategy based on the model predictive control theory. The proposed fault detection method can detect and locate a failed switch with current error signals dedicated to each switch, based on the position of space vectors. Following the fault detection, the proposed fault-tolerant control strategies are developed to produce three-phase balanced sinusoidal output voltages/currents with only normally functioning bidirectional switches. Therefore, the developed control technique for the matrix converter can preserve continuous operation even after the failure of switches and improve reliability of the drives. Simulation results are provided to validate the effectiveness of the applied detection and control strategy.

This paper proposes a new on-line fault detection method to detect open-switch faults in a Matrix Converter (MC). The proposed fault detection method is based on a two-step algorithm. The first step identifies the occurrence of the fault by monitoring the measured load currents. The second step locates the faulty switch by measuring the response of the MC to a set of predefined switching states. The proposed method allows fast detection and isolation of a faulty switch, independent of the modulation strategy, the voltage transfer ratio, or the output frequency, and without any requirement of additional voltage sensors. The paper also presents a Model Predictive Control (MPC)-based post-fault strategy that enables the MC to continue its operation during faulty conditions. Time-domain simulation studies are performed in the MATLAB/SIMULINK environment to evaluate the performance of the proposed fault detection method and the effectiveness of the MPC as a post-fault control strategy.

This paper proposes a fault detection technique for a three-stage cascaded boost converter with open-circuit switch fault occurrence. The proposed fault detection algorithm is based on the behavior investigation of the three-stage cascaded boost converter under normal and fault operating conditions. The PI controller with one outer voltage loop and two inner current loops is employed to control the operation of the converter under both healthy and faulty modes. The slopes of two inductor currents and two controlled signals generated by the current loops of the PI controller are applied to the proposed fault detection strategy in order to identify the faulted switch. There is no additional sensor needed for fault detection. The simulation results are shown to validate the effectiveness of the proposed fault detection technique under different open-circuit switch fault operating conditions.

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Shipboard microgrids (SMGs) have played a significant role in the concept of all-electric ships (AES), with increasing power demand from loads during operation. The system stability and reliability are pivotal for supporting various aspects of SMG operation. Faults on bus or transmission line in SMGs, even minor faults, can result in catastrophic consequences such as an abrupt disruption in the current, voltage, or frequency signals. Detection and classification of faults are essential and important for system restoration. To maintain system supply reliability, a wavelet transform (WT) approach is proposed in this paper to extract the faulty signal information by performing different levels of signal decomposition. The daubechies wavelet “db4” is used and implemented with various signal level decompositions to obtain the signal detailed coefficient and compare it with adaptive threshold values to detect and classify faulty phases. The fault location was carried out by obtaining the local fault information on transmission line or bus of SMGs. MATLAB Simulation results of a practical hybrid SMG are presented to ensure the performance of proposed method for fault detection and classification on transmission line.

Generally, the measurements of modern industries are collected from different sources, which indicates that the traditional multivariate statistical process monitoring methods cannot be directly used in the multirate systems if one aims to utilize the complete multirate measurements. Hence, a set of multirate factor analysis models is developed for process modeling and fault detection purpose in the multirate processes. In the proposed model, the cross correlations are described and bounded by the common factors and the model parameters are calibrated using the expectation–maximum algorithms. Also, the proposed models are further discussed both from theoretical and geometric perspective. Finally, the proposed fault detection methods are tested by a simulated Tennessee–Eastman process and a real R2S anaerobic reactor unit in the wastewater treatment process.

This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.

This paper addresses a zonotopic fault detection (FD) observer for discrete-time descriptor systems subject to additive actuator faults. The considered descriptor systems are also perturbed by unknown-but-bounded uncertainties including state disturbances and measurement noise. Under a set-based framework, the effects of uncertainties and faults are separated into two state zonotopes. Based on this decomposition, the FD observer gain is designed to be robust against uncertainties and meanwhile sensitive to faults based on a index in a finite-frequency domain. Then, two linear matrix inequality (LMI) conditions are obtained to design an observer that achieves robustness and fault sensitivity at the same time. The generalised KYP lemma is applied to address the fault sensitivity condition. The optimal FD observer gain can be obtained via an offline design procedure. Finally, the proposed fault detection method is applied to a chemical mixing system and the effectiveness is shown.

Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes