Fault Detection Scheme Research Articles

SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoT With Improved Energy Efficiency

Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-oriented binary sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoT node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoT nodes; 2) construct the fault dictionary according to the characteristics of measurements; and 3) detect faults via the fault dictionary. In addition, a binary-based sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92 B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71-B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

Improving distance relay performance for heavily distorted voltage and current by accurate signal reconstruction using extended Kalman filter

Distance relay plays a pivotal role in the detection of faults and subsequent isolation in transmission lines. However, severe distortion in the measured voltage and current waveforms (such as due to current transformer (CT) saturation), affects their performance. This paper focuses on a compensation method for signal reconstruction using extended Kalman filter (EKF) algorithm to improve relay functioning, enhancing the safety of transmission lines. The proposed fault detection scheme was tested extensively by running simulations using PSCAD and MATLAB for different types of faults at various distances and CT burden. The CT model in PSCAD was developed based on the Jiles–Atherton phenomenological theory in order to closely resemble a real-time CT with its saturation effects. The simulation results show that the proposed method is highly efficient in determining the exact fault point with improved zone approaching times even with measured signals having high noise and harmonic content. In cases where the conventional relay fails to detect the fault in the correct zone, the compensated data from the proposed method accurately detects the zone. Hardware experimental tests were carried out to further verify the effectiveness of the scheme in real-time, where the EKF based algorithm was implemented using a DSP-based microcontroller.

Reliable Architectures for Finite Field Multipliers Using Cyclic Codes on FPGA Utilized in Classic and Post-Quantum Cryptography

Fault detection is becoming greatly important in protecting cryptographic designs that can suffer from both natural or malicious faults. Finite fields over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> are widely used in such designs, since their data are coded in binary form for practical reasons. Among the different finite field arithmetic, multiplication is the bottleneck operation for many cryptosystems due to its complexity. Therefore, in this work, fault detection schemes based on cyclic codes for finite field multipliers using different fields found in traditional and post-quantum cryptography are derived. Moreover, we implement such schemes by embedding them into the original architectures to perform an exhaustive study, benchmark the different overheads obtained, and prove their suitability for deeply constrained embedded systems. These implementations are performed on advanced micro devices (AMD)/Xilinx field-programmable gate array (FPGA) and provide a very high error coverage with acceptable overhead.

Fault detection for discrete-time fuzzy systems with measurement errors using interval observers

This paper presents an interval observer-based fault detection (FD) strategy for discrete-time T–S fuzzy systems with measurement errors. The system and measurement outputs are selected as the premise variables of plant and observer respectively. The bounds of mismatch items caused by the measurement errors are established by covering matched region, mismatched left adjacent region and right adjacent region. Piecewise Lyapunov function, taking full account of possible transitions, is employed to drive observer design condition. FD is turned into optimization problem with disturbance attenuation, fault sensitivity and nonnegativity constraints. The decision is implemented by judging whether zero is excluded from the residual interval. Finally, simulation is explored to verify the scheme.

Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach

Maintaining electrical machines in good working order and increasing their life expectancy is one of the main challenges. Precocious and accurate detection of faults is crucial to this process. Induction motors (IMs) are among these machines widely utilized in various fields including industrial and domestic applications that require effective detection of their status. This paper proposes a novel fault recognition and correction (FRC) scheme based on the internet of things (IoT) and deep learning for IMs. In the developed system, vibration signals during motor operation are used to generate bearing fault features, which are inputted to the designed deep learning model to successfully identify bearing faults. The robustness of the proposed approach is tested against a false data injection (FDI) attack. Further, the proposed deep learning approach has been assessed and compared with other state-of-the-art algorithms available in the literature. Experimental testing has been carried out on a real IM to perform the suitability of the developed fault detection and correction scheme. Compared to other fault recognition techniques, the proposed method proves to be more effective. In essence, the results verified the robustness of the novel proposed strategy against the FDI attack making it possible to recognize faults with confidence and improve decision-making to determine the motor's status.

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Abstract Condition monitoring of railway point machines is important for train operation safety and effectiveness. Referring to the fields of mechanical equipment fault detection, this paper proposes a fault detection and identification strategy of railway point machines via vibration signals. A comprehensive feature distilling approach by combining variational mode decomposition (VMD) energy entropy and time- and frequency-domain statistical features is presented, which is more effective than single type of feature. The optimal set of features was selected with ReliefF, which helps improve the diagnosis accuracy. Support vector machine (SVM), which is suitable for a small sample, is adopted to realize diagnosis. The diagnosis accuracy of the proposed method reaches 100%, and its effectiveness is verified by experiment comparisons. In this paper, vibration signals are creatively adopted for fault diagnosis of railway point machines. The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.

A data‐driven distributed fault detection scheme based on subspace identification technique for dynamic systems

AbstractWith the aid of the subspace technique and the average consensus algorithm, the main objective of this article is to develop a data‐driven design of distributed fault detection for dynamic systems using the measurement in a complex sensor network. Specifically, the design process consists of two stages: distributed off‐line learning and distributed online fault detection. Among them, the distributed off‐line learning stage involves the average consensus algorithm and parameter identification by subspace technique. It is worth mentioning that, the distributed fault detection approach has the same performance as the centralized fault detection approach and avoids complex information exchange. In the end, a numerical simulation example and a case study of the three‐phase flow facility are illustrated to show that the proposed distributed approach can accomplish the fault detection task successfully.

Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors

The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams.

Identifying the influence of user behaviour on building energy consumption based on model-based analysis of in-situ monitoring data

This study carries out a comprehensive comparison between design and monitoring results in a building located in Innsbruck, Austria, with the focus on the user influence as an important part of a fault detection strategy. The space heating consumption shows a difference from the design (of 11 kWh/(m2a)), with an excess of 22 kWh/(m2a) in 2019 and 28 kWh/(m2a) in 2020. Contrarily, monitored energy consumption for domestic hot water and appliances are lower than the design. Electricity consumption for mechanical ventilation and photovoltaic production are similar to the design. A fault detection and diagnosis analysis is carried out at building and room level (apartments and common rooms), studying the correlation between the energy consumption and temperature of the corresponding space. Several methods are tested to identify the rooms with non-standard behaviour with respect to window ventilation, setpoint temperature and a combination of them and their influence on the space heating demand. The analysis assesses an influence of the occupants behaviour (in terms of higher window ventilation and setpoint temperature) of 20–32% on the space heating demand, which does not entirely justify the high mismatch between measured and design values. Combinations of known, likely and possible causes for the higher monitored space heating are investigated and show the upper and lower bound of user behaviour and building deficiencies.

Deep Transfer Learning Framework for Bearing Fault Detection in Motors

The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.

Asynchronous Frequency-Dependent Fault Detection for Nonlinear Markov Jump Systems Under Wireless Fading Channels.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed l2 gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.

A reconstruction based adaptive fault detection scheme for distribution system containing AC microgrid

The occurrence of a single line to ground fault, including high impedance faults (HIFs), in various grounding systems,results in a low magnitude fault current. Due to this, the present protection device has trouble finding the fault and faulty feeder. This manuscript attempts to propose a new fault detection strategy based on a sliding window-based recursive matrix pencil method to address this problem. The suggested technique starts by utilizingrecursive matrix pencil method to reconstruct the fault current signal, and after that, it calculates the transient error by deducting the reconstructed fault current signal from the original signal. This error serves as a fault detection index, which is also utilized to categorize the defective feeder. The suggested approach does not call for the computation of any zero sequence components and does not entail any extra voltage-based criteria, in contrast to existing techniques. For three different distribution networks with AC microgrids that were modeled using EMTDC/PSCAD 5.0, the superiority of the given technique was tested. Results, which were acquired by simulating several worst-case scenarios, demonstrate the effectiveness of the strategy being discussed (fault and non-fault). Additionally, the applicability of the proposed protective mechanism is supported by the results of two field test cases, which validate its use in actual situations.

Fault detection and isolation for a class of uncertain nonlinear systems: A dual-monitoring model-based approach

This paper presents a fault detection and isolation (FDI) scheme for a class of uncertain nonlinear systems with unknown faults, in which fault isolation process and control reconfiguration are integrated via a supervisory switching strategy. A group of adaptive estimators is performed together with the switching process. The proposed FDI approach, namely dual-monitoring model-based approach, can exploit information both from backstepping procedure and estimators, by which a set of monitoring functions is constructed to detect the occurrence of fault and also identify the specific fault type. In particular, based on some principles, the fault isolation process can be accelerated under a novel interacted isolation scheme. It can be seen that the tracking error can be always guaranteed within a prescribed performance in the existence of system faults.

Fault Tolerant Control of Quadrotor Based on Sensor Fault Diagnosis and Recovery Information

Drones have been developed for more than two decades. They have become central to the functions of various civil aviation and military services. Commercial usage of drones continues to grow steadily. As the drones have been used widely in different areas, this raises a safety concern, i.e., all the multi-rotors have an increased risk of motor or sensor faults. This paper considers a fault-tolerant control (FTC) problem against the inertial motion unit (IMU) sensor fault. First, a neural network estimator is built for the purpose of fault diagnosis. Second, a fault detection scheme is designed by comparing the IMU reading with the estimator, where it uses a logic rule to monitor the IMU state. Third, if the IMU sensor is in faulty state, the Euler angle estimator with neural network built is used to recover the IMU information which is fed into the controller designed. Finally, simulation studies are given to illustrate the effectiveness of the proposed FTC.

High Impedance Fault Detection Protection Scheme for Power Distribution Systems

Protection schemes are used in safe-guarding and ensuring the reliability of an electrical power network. Developing an effective protection scheme for high impedance fault (HIF) detection remains a challenge in research for protection engineers. The development of an HIF detection scheme has been a subject of interest for many decades and several methods have been proposed to find an optimal solution. The conventional current-based methods have technical limitations to effectively detect and minimize the impact of HIF. This paper presents a protection scheme based on signal processing and machine learning techniques to detect HIF. The scheme employs the discrete wavelet transform (DWT) for signal decomposition and feature extraction and uses the support vector machine (SVM) classifier to effectively detect the HIF. In addition, the decision tree (DT) classifier is implemented to validate the proposed scheme. A practical experiment was conducted to verify the efficiency of the method. The classification results obtained from the scheme indicated an accuracy level of 97.6% and 87% for the simulation and experimental setups. Furthermore, we tested the neural network (NN) and decision tree (DT) classifiers to further validate the proposed method.

High speed fault detection and localization scheme for low voltage DC microgrid

In this paper, a new differential protection scheme based on the sensitivity of the relay is proposed for fault detection and location estimation in DC microgrid having different renewable energy sources (RESs) namely PV, wind and battery. The sensitivity of each relay, connected at the bus terminal of the microgrid, is determined by using the magnitude of currents flowing through the cables. Once, Sensitivity factor (S-factor) for each relay is calculated, proposed scheme detects the fault by comparing S-factor of each relay with predefined threshold value. Further, the fault location is determined by measuring voltage and current at both ends of the faulty cable. The effectiveness of the proposed scheme is validated on a 380 V ring-type DC microgrid simulated under various fault conditions with different fault resistance values and at different fault locations. The simulation results obtained under different operating conditions indicate high selectivity and high speed of operation (less than 1.2 ms) of the proposed scheme for fault detection and isolation of faulty part. The proposed scheme ensures the effectivness and reliability when compared with other schemes mentioned in the literature.

Model-Free Fault Detection Based on Performance Residual for Feedback Control Systems

This brief proposes a control performance-oriented fault detection (PFD) strategy for closed-loop linear time-invariant systems. Firstly, the performance index with the summation of quadratic form over the infinite time interval is investigated in virtue of a data-driven residual-centered closed-loop model, which can be constructed utilizing input and output data instead of system models. Then the adaptive residual generator is presented to update the parameters of the data-driven model in case of system changes caused by anomalies, serving the information of the faulty plant for PFD. Further, the performance residual, which represents the performance degradation, is defined based on the Bellman equation. By embedding the predesigned controller parameters in the performance residual generator, the performance degradation caused by system faults and controller mismatches can be detected. Additionally, the proposed scheme can be applied in various operating points without revising the detector parameters. Finally, a randomized algorithm-aided threshold design is developed to handle unknown input. Case studies on a circuit model are conducted to show the superiority of PFD.

A Novel Distributed Data-Driven Strategy for Fault Detection of Multi-Source Dynamic Systems

In this brief, a distributed data-driven fault detection method based on sparse integrated dynamic principal component analysis is designed to enhance the fault detection performance of multi-source dynamic systems. First, by dividing the complex system into multiple sub-sources according to the structural principle, the fusion and complementation of process mechanism knowledge and data-driven are realized. Second, by introducing the Kronecker product covariance structure, the individual modes within each sub-source as well as joint modes shared between sub-sources are captured. Notably, by developing a truncated contribution power method, key variables are selected and the interpretability of principal components is improved. Finally, experimental results demonstrate that the proposed strategy can significantly improve the detection performance compared with the existing PCA-based methods.

An Inter-Turn Short-Circuits Fault Detection Strategy Considering Inverter Nonlinearity and Current Measurement Errors for Sensorless Control of SPMSM

Aiming at the misdiagnosis problem of the interturn short-circuit (ITSC) fault detection method caused by the inverter nonlinearity and the current measurement errors, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis residual voltage-based fault diagnostic strategy is proposed for sensorless control of the surface permanent magnet synchronous motor (SPMSM). The model of the three-phase wye-connected SPMSM with ITSC fault is established, and the expression of the d-axis residual voltage caused by the ITSC fault, the inverter nonlinearity, the current measurement errors and the motor parameter errors is derived. The proposed fault diagnostic strategy can eliminate the current relative gain coefficient error and locate the ITSC fault by using the eighth and second harmonics of the estimated d-axis residual voltage, respectively. The experimental results verify the effectiveness of the proposed fault diagnostic strategy.

Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies.