Track fault detection method using train running sound

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.

The increasing importance of power electronic converters in supplying electrical energy to utility grids places a higher priority to detect and protect against fault conditions. Fault detection and isolation are particularly important for inverters that provide black-start recovery for microgrids since these converters provide the energy source for restoration after a power outage. This paper presents a new fault detection and location method for Cascaded B-Bridge (CHB) multilevel inverters. The new fault detection method is based on monitoring the output voltage of each cell and output current directions along with each switch's state. By monitoring each cell's output voltage and current direction, the faulty cell can be detected and isolated. After the faulty cell is detected, the faulty switch can be located by comparing the current direction with the switching states. This technique is implemented with Level-Shifted Pulse Width Modulation (LS-PWM) in order to maintain acceptable total harmonic distortion (THD) levels at the converter. The proposed method can be implemented for a CHB with any number of cells, can operate with nonlinear loads, and offers very fast detection times. Simulation and experimental results verify the performance of this method.

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field.

This chapter deals with the recent advancements in IGBT fault detection and protection methods for modular multilevel converters (MMCs). First, the IGBT short-circuit fault and open-circuit fault are briefly introduced. Then, the detection and protection methods are present for the IGBT short-circuit faults. Following the introduction of the features of the submodule's IGBT open-circuit faults, several fault detection methods are presented for the MMC, including a Kalman filter based method, an integrator-based method, a sliding-time window based method, and an isolation forest based method. Finally, a conclusion is given for the chapter.

In the field of industrial maintenance, early detection of gearbox faults is crucial, yet challenging, especially under varying and unseen operating conditions and types of damage. Traditional gear fault detection methods are typically designed for known operating conditions. However, in real industrial scenarios, gear fault detection is always an open-set detection, in which working conditions are commonly unseen. To overcome these limitations, this study introduces the ResNetAutoencoder Fault Detection (RAFD) method. RAFD integrates a short-time Fourier transform with a pre-trained ResNet-18 model for feature extraction and an autoencoder for feature reconstruction, trained on normal operational data. Moreover, a similarity loss is introduced to minimize the distribution gap of different working conditions. The effectiveness of the method is evaluated against traditional autoencoder and one-class support vector machine across various operational conditions, with performance metrics including AUROC, precision, recall, and F1 -score. The results demonstrate RAFD's superior performance in both in-distribution and out-of-distribution tasks, underscoring its robustness and reliability in detecting normal and faulty gearbox states.

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

This paper discusses gate open-circuit fault detection and diagnosis method in SPWM modulated symmetric cascaded H-bridge multilevel inverter. First, an output voltage modelling under fault condition in cascaded H-bridge multilevel inverter is presented. Further, simulations of fault conditions have been carried out for the development of the fault detection and diagnosis method. Based on fault modelling and simulations, a fault detection and diagnosis method have been proposed based on the mean values of the bridge voltages as the diagnostic feature. Further, the proposed method has been validated experimentally on a 5-level symmetric sine pulse width modulated cascaded H-bridge multilevel inverter.

In this paper, a simple and fast open-phase fault (OPF) detection and fault tolerant control method is proposed for three-phase induction machine. Instead of using integrators or low pass filter, this method utilizes the phase current magnitude and trajectory to detect open phase fault in three-phase motor drive. This significantly increases the speed of fault detection, allowing an OPF to be detected and compensated within several sampling cycles, which is advantageous for safety-critical applications. In addition, a double synchronous reference frame PI (DSRF-PI) controller method is adopted as the fault tolerant controller. Compared to previous methods that utilizes feedforward voltage compensation, the DSRF-PI does not require prior knowledge on the machine parameters and operating condition, and therefore is easier to implement. The proposed fault detection and fault tolerant control method are simulated using MATLAB Simulink simulation where the results confirms the high speed and robustness of the proposed methods.

For an efficient data transmission between logic die and core die in HBM DRAM, Through-Silicon-Via (TSV) is an essential part that connects stacked memories. TSV is a vertical component which cannot be found in the conventional 2D memory. It plays an important role in improving integration in 3D memory structure. However, due to its temporal variation and technological weakness, error cases occur frequently in TSV. Following this, to increase the reliability of the memory operation, fault detection and correction method for TSV is a new challenge in 3D memory. For the efficient fault correction, fault detection method that classifies hard fault and soft error has been studied. In this paper, a new TSV fault detection method using error pattern analysis will be presented. As a result, the proposed scheme shows higher detection success rate with low area overhead, compared to the conventional scheme.

Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts, and misalignment developed on the railways due to pre-emptive investigations, non-maintenance, and delay in detection pose grave threats and danger to the safe operation of railway transportation. In the past, manual inspection was performed for the rail track by a rail cart which is both prone to error and inefficient due to human biases and error. Several train accidents are reported in Pakistan; it is important to automate these techniques to avoid such train accidents for the safety of countless lives. This study aims to enhance railway track fault detection using an automatic rail track fault detection technique with acoustic analysis. Moreover, the proposed method contributes to making the dataset large by using the CTGAN technique. Results show that acoustic data may help to determine the railway track faults effectively and logistic regression is used to perform the classification for railway track faults with an accuracy of 100%.

A Data-Driven Fault Detection and Diagnosis by NSGAII-t-SNE and Clustering Methods in the Chemical Process Industry

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

By taking rotary machine as the research object, a fault detection method based on improved capsule network and adaptive wavelet noise reduction is proposed to guarantee the stability of daily operation of mechanical equipment. Among them, capsule network is used as the basic fault detection method, which is improved by introducing residual module and other methods. In addition, the fault detection performance is further improved by combining the method of adaptive wavelet noise reduction. The experimental results show that after introducing adaptive wavelet noise reduction method, the detection accuracy of the constructed detection method in noisy environments is significantly improves, indicating that the introduction of adaptive wavelet noise reduction is necessary. Compared with other commonly used detection methods, the fault detection method based on improved capsule network and adaptive wavelet noise reduction has better fault detection performance, and the detection accuracy reaches 99.89%. The fluctuation range is 99.89% ± 0.15%, with good stability. Meanwhile, in the noisy environment, the detection accuracy of the method fluctuates less, indicating that it has better anti-noise ability. In summary, the equipment fault detection method based on improved capsule network and adaptive wavelet noise reduction has excellent performance and good anti-noise ability, and it can be applied to the actual working scene of rotary machine for fault detection, effectively ensuring the normal operation of the equipment.

The incipient fault detection of a complex industrial process is a challenging problem for traditional dynamic detection methods. Traditional dynamic detection methods usually decouple the correlations among the variables and dynamic correlations simultaneously, which makes the two types of correlations mixed and may lead to performance deterioration in long-sequence dynamic detection. Some incipient faults may not change the amplitudes of process variables but change the long-sequence dynamic features. Based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> statistic and matrix multiplication transformation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT), traditional dynamic detection methods can detect many faults effectively. However, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT can not effectively detect some incipient faults due to the above two types of correlations mixed. In order to overcome the shortcomings of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> S-MMT and improve the detection ability of some incipient faults, this paper proposes a fault detection method based on the dynamic k-nearest neighbor model and Dual Control Chart (DKNN-DCC), which can improve the incipient fault detection performance by using long-sequence dynamic detection. The proposed method is verified by the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process. The experimental results show the effectiveness of the proposed method in incipient fault detection compared with traditional dynamic detection methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper presents a novel incipient fault detection method, which directly mines the long-sequence dynamic abnormal information from the process variable and overcomes the problem of some abnormal information being submerged in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2}$</tex-math> </inline-formula> statistic calculated based on the matrix multiplication transformation. The proposed method can detect incipient faults that are not easily detected by some traditional methods and can help operators find the abnormal and avoid more serious losses. The structure of the proposed method jumps out of the frameworks of traditional dynamic detection methods, which is feasible to apply to different stable industrial processes.

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling <TEX>$T^2$</TEX> index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.