Traditional Fault Diagnosis Methods Research Articles

A new method for fault identification of T-connection transmission line based on multi-scale traveling wave reactive power and random forest.

Though the traditional fault diagnosis method of T-connected transmission lines can identify the faults inside and outside the area, it can not identify the specific branches. To improve the accuracy and reliability of fault diagnosis of T-connection transmission lines, a new method is proposed to identify specific faulty branches of T-connection transmission lines based on multi-scale traveling wave reactive power and random forest. Based on the S-transform, the mean and sum ratios of the corresponding short-time series traveling wave reactive powers of each two traveling wave protection units at multiple frequencies are calculated respectively to form the fault feature vector sample set of the T-connection transmission line. A random forest fault branch identification model is established, and it is trained and tested by the fault feature sample set of T-connection transmission line to identify the fault branch. The simulation results show that the proposed algorithm can identify the branch where the fault is located inside and outside the protection zone of T-connection transmission line quickly and accurately under various working conditions. This method also shows good performance to identify faults even under the situation of CT saturation, noise influence and data loss.

Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings

Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively.

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

Digital twin model for chiller fault diagnosis based on SSAE and transfer learning

The equipment of chiller systems is characterized by a complex mechanical structure and operating environments that vary widely, resulting in high failure rates, energy waste, and costly maintenance. Traditional fault diagnosis methods suffer from low levels of digitalization and intelligence. Therefore, timely and accurate detection and diagnosis of the operating status of chiller systems are critical. To address the above issues, this study proposes a digital twin (DT) model for chiller system fault diagnosis based on stacked sparse auto-encoder (SSAE) and transfer learning (TL). First, a chiller system digital twin mapping model is constructed utilizing digital twin technology. Then, the SSAE model is constructed to provide real-time defect diagnosis and fault result validation. Finally, to address the limited chiller system data in practical applications, the knowledge of fault diagnosis acquired in the source domain is transferred to the target domain via TL. Experimental results demonstrate that the SSAE model outperforms back propagation (BP), recurrent neural network (RNN) and long short-term memory (LSTM) model in term of accuracy. Furthermore, using TL achieves a diagnostic accuracy of over 90% for different degrees of fault severity, with a significant decrease in cross-entropy loss compared to no TL, confirming the effectiveness of the TL method.

Research on fault diagnosis method of electric gate valve under strong background noise

Electric gate valves are essential equipment in nuclear power plants. The safe and stable operation of an electric gate valve is key in maintaining the safety of nuclear power plant. However, due to the strong surrounding noise during the signal acquisition process that affect nuclear power plant electric gate valves, the early status monitoring and fault diagnosis of the electric gate valve are ineffective. However, the traditional fault diagnosis methods are not suitable for electric gate valves and fail to classify faults classification under the strong background noise. To achieve the noise reduction of electric gate valve acceleration signals based on strong background noise, the work proposes a Variational Mode Decomposition (VMD) method to denoise the strong noise in the collected signal. Seven genetic optimization algorithms are combined with VMD to enhance the model parameter selection of VMD. This selection criterion helps to select the most suitable algorithm combination for processing the acceleration signal of the electric gate valve under the background of strong noise. In order to obtain a better fault classification effect, the weak noise in the acceleration signal is further removed by Singular Value Decomposition (SVD). Moreover, the time-domain feature parameter extraction is performed on the denoised acceleration signal. Consequently, the model training of the Bidirectional Gate Recurrent Unit (BGRU) and the development of the electric gate valve fault diagnosis system under the background of strong noise are completed. The final experimental results show that the system developed in this paper has a good fault classification effect.

Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement

Deep-learning-based identification and diagnosis of faults in industrial assets has received much attention. It does not need a deep understanding of the target domain or complex signal-processing techniques for time-consuming feature identification and selection. The vast majority of approaches developed in this field rely on public data recorded in laboratory conditions with high-performance measurement equipment. Moreover, primarily vibration data is used for condition monitoring tasks, which is particularly sensitive to typical error patterns in rotating machinery. These conditions are difficult to maintain in industrial environments since using high-performance measurement systems would not be economically feasible. This paper demonstrates that modern deep learning can achieve extraordinary fault detection results with industrial standard low-cost measurement systems. We determine measurements taken from the industrial-sensory equipment that potentially capture the system’s fault patterns. Hereafter, we apply traditional failure detection and identification techniques to evaluate the suitability of the taken measurements for bearing fault detection. These techniques apply envelope analysis to reveal the fault patterns in the signals and a support vector machine to separate the fault classes. We can conclude that the used low-cost sensory equipment is capable to capture meaningful, fault-describing patterns. To improve the failure detection performance we are investigating a 2D, as well as 1D, convolutional neural network approach to identify the error patterns and classify the respective errors. We compare the deep-learning-based methods with the traditional methods. Furthermore, we assess which inverter signal carries the largest fault-describing information content. Experimental results indicate that the proposed deep learning methods outperform traditional fault diagnosis methods, hence, demonstrating the effectiveness in an industrial condition monitoring application.

Bearings fault diagnosis method based on MAM and deep separable dilated convolutional neural network

Abstract Aiming at the problems of traditional fault diagnosis methods that do not represent the time correlation between signals, low recognition accuracy under complex working conditions and noise interference and too many parameters, a bearing fault diagnosis method based on mixed attention mechanism (MAM) and deep separable dilated convolution neural network (DSDCNN) is proposed. Firstly, a Markov transfer field encoding method is used to transform the original one-dimensional vibration signal into a two-dimensional feature image with temporal correlation. Secondly, a deep separable convolution algorithm is presented by taking advantage of the low computational complexity of deep separable convolution and the ability of dilated convolution to expand the receptive field under the condition of invariable number of parameters. Then, the MAM is designed to make the model capture the feature dependency of the feature map in spatial and channel dimensions, and the MAM-DSDCNN model is constructed. Finally, the fault diagnosis performance of the proposed model is verified with two different data sets. The results show that the average recognition accuracy of MAM-DSDCNN reaches 99.63% under variable load conditions, 99.42% under variable speed conditions, 94.26% under noisy environment with the signal-to-noise of 0 dB, which prove that the model has higher recognition accuracy, stronger generalization and noise immunity performance than other deep learning algorithms.

Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved Transformer

Aiming at the problems of low accuracy and robustness of traditional deep learning fault diagnosis methods, a novel attention-based multi-feature parallel fusion model Diagnosisformer is proposed for rolling bearing fault diagnosis utilizing Transformer as the basic network. Firstly, frequency domain features of the original data are extracted by Fast Fourier Transform (FFT), and then normalization operations and embeddings are performed on the model input. Secondly, the designed multi-feature parallel fusion encoder is exploited to extract the local and global features of the bearing data. The extracted features are fed to a cross-flipped decoder, followed by a classification head for fault classification. Finally, experimental verification is performed using data collected by the rotating machinery fault diagnosis experimental platform and the Case Western Reserve University (CWRU) bearing dataset. The average experimental results on the two fault diagnosis datasets are 99.84% and 99.85%, respectively. The results show that our diagnosis method significantly outperforms the state-of-the-art in accuracy, generalization, and robustness.

A Deep Learning Method for Rolling Bearing Fault Diagnosis Based on Attention Mechanism and Graham Angle Field.

Focusing on the low accuracy and timeliness of traditional fault diagnosis methods for rolling bearings which combine massive amounts of data, a fault diagnosis method for rolling bearings based on Gramian angular field (GAF) coding technology and an improved ResNet50 model is proposed. Using the Graham angle field technology to recode the one-dimensional vibration signal into a two-dimensional feature image, using the two-dimensional feature image as the input for the model, combined with the advantages of the ResNet algorithm in image feature extraction and classification recognition, we realized automatic feature extraction and fault diagnosis, and, finally, achieved the classification of different fault types. In order to verify the effectiveness of the method, the rolling bearing data of Casey Reserve University are selected for verification, and compared with other commonly used intelligent algorithms, the results show that the proposed method has a higher classification accuracy and better timeliness than other intelligent algorithms.

An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution

Bearings are an essential component of rotating mechanical equipment. Traditional signal processing-based fault diagnosis methods usually require a massive labeled data for training, but bearings generally operate in the equipment under normal fault-free conditions. This paper proposes an improved adaptive batch normalization (AdaBN) transfer learning bearing fault diagnosis method for batch normalization (BN) in traditional deep learning architecture. The AdaBN network preprocesses the raw vibration signals, and then the preprocessed features are input to a depthwise sparable convolution neural model for training. Features are extracted by depthwise convolution and point convolution in the network. AdaBN can freeze all the parameters in the network except the BN layer. Finally, a small amount of labeled data is classified using transfer learning methods. A laboratory data set was used for validation, and the experimental validation showed that the accuracy of the bearing fault diagnosis method using AdaBN reached 85%.

A fault location method based on ensemble complex spatio-temporal attention network for complex systems under fluctuating operating conditions

Recently, traditional fault diagnosis methods achieve fault identification by establishing a sample set covering all fault degrees of different fault components in complex systems. However, the uncertainties including environmental stresses and own physical and chemical variations lead to an infinite number of fault degrees of fault components, and the fault identification of complex systems in practical engineering applications faces the challenge of fluctuating operating conditions due to variations of rotational speed and load. To address the above problem, a fault location method based on ensemble complex spatio-temporal attention network (ECSAN) is proposed in this paper, which can identify the critical fault components of complex systems by combining an ensemble learning mechanism with excellent basic estimators. A basic estimator consists of a feature extraction module, a feature enhancement module, and a classification module. In the first module, a complex spatio-temporal backbone network with strong generalization is developed to provide spatio-temporal features containing the inherent information of the sample data for complex systems. In the feature enhancement module, a lightweight complex attention layer is constructed to enhance the effective structural information of the features and reduce the interference of their redundant information. The classification module then adopts a Softmax layer to perform fault classification. Finally, an ensemble learning mechanism is designed to integrate the basic estimators. By constructing sample weights and introducing a knowledge transfer strategy, the generalization is further improved while saving training expenses. Two datasets from different experimental platforms are concerned to verify the effectiveness and superiority of this method under various operating conditions and fault degrees. The experimental results indicate that this method achieves 99.81% accuracy on a standard dataset of the mechanical system and 98.88% accuracy on a real dataset of the closed-loop control system of the water jet propulsion device, which is superior to comparison approaches.

Cyclogram: an effective method for selecting frequency bands for fault diagnosis of rolling element bearings

Frequency band selection for repetitive transient extraction using the kurtogram and its variants plays a vital role in fault diagnosis of rolling element bearings. However, cyclostationarity, one of the most typical symptoms of faulty bearings, is always neglected in these methods, leading to failure of the extraction of the weak fault features. To address this shortcoming, a novel method for selecting frequency bands, called Cyclogram, is here proposed based on kurtosis and cyclostationarity. In the proposed method, a signal is decomposed into several signals in different frequency bands by a wavelet packet transform, and squared envelopes (SEs) are calculated for these decomposed signals. Then, a robust indicator of SEs for evaluating repetitive transients is constructed based on cyclic spectral coherence and kurtosis, which helps to select useful frequency bands. Afterwards, the envelope spectrum of these selected frequency bands are averaged rather than only selecting one frequency band to enhance fault features. Compared with traditional fault-diagnosis methods for rolling element bearings, the proposed method is able to identify faults from signals corrupted seriously with Gaussian and non-Gaussian noise. The effectiveness of Cyclogram is validated based on simulation and three real-world vibration signals from faulty bearings.

Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.

Signal-to-Image: Rolling Bearing Fault Diagnosis Using ResNet Family Deep-Learning Models

Rolling element bearings (REBs) are the most frequent cause of machine breakdowns. Traditional methods for fault diagnosis in rolling bearings rely on feature extraction and signal processing techniques. However, these methods can be affected by the complexity of the underlying patterns and the need for expert knowledge during signal analysis. This paper proposes a novel signal-to-image method in which the raw signal data are transformed into 2D images using continuous wavelet transform (CWT). This transformation enhances the features extracted from the raw data, allowing for further analysis and interpretation. Transformed images of both normal and faulty rolling bearings from the Case Western Reserve University (CWRU) dataset were used with deep-learning models from the ResNet family. They can automatically learn and identify patterns in raw vibration signals after continuous wavelet transform is used, eliminating the need for manual feature extraction. To further improve the training results, squeeze-and-excitation networks (SENets) were added to improve the process. By comparing results obtained from several models, we found that SE-ResNet152 has the best performance for REB fault diagnosis.

Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks.

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.

Fault diagnosis for dynamic system based on the independent latent space reconstruction of generative adversarial network

Data-driven fault diagnosis for dynamic process faces three challenges. Firstly, models are hard to establish to describe the multivariate coupled correlations. Secondly, in an actual industrial process, the cost of fault data collection is huge, and fault training data is often scarce. Fortunately, a lot of normal operating data can be sorted, however, traditional fault diagnosis methods have insufficient processing capabilities for big data which is the third challenge. In view of the above problems, an improved long short-term memory generative adversarial network (LSTM-GAN) is introduced to utilize the time sequence normal big data to train a fault detection model and the reconstruction error of generator and inverter is proposed as the fault indicators. Then, independent latent space is obtained by constructing an inverter and the fault related subspace is extracted for fault type diagnosis. Experiment results based on the benchmark dataset of the Tennessee Eastman Process (TEP) show the effectiveness of the proposed method from the aspects of false alarm rate, miss alarm rate and diagnostic accuracy.

Circuit Breaker Fault Diagnosis Method Based on Coil Current Time Sequence Phase Trajectory Characteristics

The traditional circuit breaker fault diagnosis method suffers from insufficient feature information extraction and is easily affected by abnormal signal acquisition. To address this, this paper introduces the phase space reconstruction algorithm to reconstruct the current signal for fault diagnosis based on phase trajectory features. The proposed method uses a first-order forward differencing method and mutual information method to process abnormal data and select the parameters of the reconstruction, then extract overall and local inflection point features to construct a fault feature set. The support vector machine algorithm-based model is trained and tested using actual samples, and the results show that the proposed method can adaptively sample anomalous signals, exhibit strong robustness, and significantly improve the accuracy of fault classification.

Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin

Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines’ operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization–support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.

Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis.

Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel-space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.