Multi-sensor Fault Diagnosis Research Articles

SRSGCN: A novel multi-sensor fault diagnosis method for hydraulic axial piston pump with limited data

Deep learning has immense potential in ensuring the safe operation of hydraulic axial piston pumps (HAPP). However, the complex operating environment and high cost of labeling have resulted in a scarcity of labeled fault samples. This paper proposes a novel method called Siamese Random Spatiotemporal Graph Convolutional Network (SRSGCN). Firstly, based on graph convolutional networks, a Random Spatiotemporal Graph (RSG) is designed to aggregate multi-sensor information at different time stamps, fully exploiting the spatiotemporal features of the original data. Secondly, the Siamese Neural Network (SNN) is improved by retaining the twin subnetwork structure and removing the similarity output part. While preserving feature extraction capabilities, it is endowed with classification ability. Based on its strong feature mining capability, SRSGCN can fully utilize the scarce sample information to improve diagnostic accuracy. Finally, a case study was conducted on our HAPP experimental platform. The experiments show that compared with other existing methods, this method has higher diagnostic accuracy and can effectively solve the problem of HAPP fault diagnosis under limited data conditions.

Distributed chaotic bat algorithm for sensor fault diagnosis in AHUs based on a decentralized structure

The effective operation of sensors in heating, ventilation and air conditioning (HVAC) systems to promote high-efficiency, energy-saving and low-risk intelligent buildings. However, most existing methods are either based on a centralized architecture or employ only pure digital simulation platforms for HVAC sensor fault diagnosis and verification, which are difficult to meet the complex and dynamic needs of HVCA systems. To overcome the difficulty in employment of centralized architecture involved multi sensor fault diagnosis and fault diagnosis methods limited by pure digital simulation platform, a fully distributed chaotic bat algorithm is proposed to diagnosis multiple-sensor faults and is verified by the hardware-in-the-loop simulation platform in HVAC systems. First, inspired by the bat swarm intelligence and multiple agents, we characterize the sensor fault diagnosis problem as an optimization problem of the bat predation behavior. Specifically, each sensor node is abstracted as a bat colony, and each bat colony communicates with directly connected neighboring bat colony in physical space. Second, to avoid the tendency of bat algorithm to fall into locally optimal solutions, the tent chaotic map is employed to enhance the bat colony search ability for improving the randomness of bat algorithm, involves escaping from local extreme points and increasing the diversity of the bat colony. Finally, we embed the temperature and humidity sensors into the hardware-in-the-loop simulation platform to construct the near-actual physical environment. The performance and convergence of the proposed method are verified using both a pure digital simulation platform and a built-in hardware-in-the-loop simulation platform. Compared with basic bat algorithm (BA), particle swarm optimization algorithm (PSO) and chaotic bat algorithm (CBA), the proposed fully distributed chaotic bat algorithm (DCBA) have higher accuracy and can achieve the 98.41 % accuracy in fixed bias (FB) sensor faults, 96.96 % accuracy in complete failure (CF), 95.84 % in drift bias (DB) and 96.08 % accuracy in accuracy drops (AD).

An End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism for Multisensor Fault Diagnosis

Deep clustering methods have found successful applications in single-sensor data fault diagnosis. However, most of these methods employ separate optimization strategies that overlook the interaction between feature learning and clustering. Moreover, conventional deep learning methods for fault diagnosis often disregard the consistent and complementary information inherent in the multisensor data, resulting in unsatisfactory multisensor fault diagnosis performance. In this study, we introduce a novel End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism, termed EDCM-CCAM, tailored for multisensor fault diagnosis. Firstly, multiple deep autoencoder networks are utilized to concurrently extract the deep representation features from various sensor inputs. Secondly, we introduce a Consistency and Complementarity Attention Mechanism (CCAM) to facilitate multisensor feature fusion, accompanied by the design of two distinct loss functions to fully exploit the consistent and complementary information within multisensor data. Finally, fault pattern recognition in multisensor data is accomplished through Kullback-Leibler (KL) divergence-based clustering, while a joint optimization strategy is employed to simultaneously optimize all components of the EDCM-CCAM. The efficacy of the proposed method is validated on a gearbox dataset, demonstrating superior performance in multisensor fault diagnosis compared to alternative methods.

A Cooperative Convolutional Neural Network Framework for Multisensor Fault Diagnosis of Rotating Machinery

A Cooperative Convolutional Neural Network Framework for Multisensor Fault Diagnosis of Rotating Machinery

Bidirectional Shrinkage Gated Recurrent Unit Network With Multiscale Attention Mechanism for Multisensor Fault Diagnosis

Bidirectional Shrinkage Gated Recurrent Unit Network With Multiscale Attention Mechanism for Multisensor Fault Diagnosis

Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning

Rotating machines frequently undergo various faults causing increased maintenance and operation costs. To minimize these costs, effective and intelligent methods are thus required. Different sensor modalities reflecting various faults should continuously be monitored and interpreted to enable these methods. In this work, two sensor modalities, Infrared Thermography (IRT), and vibration are used complementary to form a multi-sensor fault diagnosis system. This system is used to diagnose the three most occurring faults: misalignment, unbalance, and rotor disk eccentricity, as single, dual, and multi-faults in a rotating mechanical system. The high feature processing capabilities of a Deep Convolutional Neural Network (DCNN) and the high predictive capabilities of a Support Vector Machine (SVM) are combined along with the potential dimensionality reduction using Principal Component Analysis (PCA). The results show that the proposed method is robust and signifies its reliability towards the effective diagnosis of considered faults. Further, IRT-based fault diagnosis outperforms the vibration-based classification in all working conditions.

A multi-sensor fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network

Recently, the fault diagnosis of rotating machinery based on deep learning has achieved increasingly widespread applications. However, it is often difficult to achieve the expected results by relying on a single sensor due to the limited information obtained by the single sensor and the susceptibility to the influence of the additive noise. To address the above problems, this paper proposes a multi-sensor fusion fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network to enhance feature learning while achieving multi-sensor data fusion. This method includes a data pre-processing module, a fusion module and a fault recognition module. In the first module, a complete ensemble empirical mode decomposition with adaptive noise algorithm is introduced to decompose and reconstruct the multi-source sensor signals, thereby reducing the impact of environmental noise on data quality. In the fusion module, a data fusion algorithm based on improved fuzzy support is designed to achieve the data-level fusion of multi-source sensors. By introducing the self-normalized properties into the convolutional structure with bi-directional gated recurrent unit, a self-normalized spatio-temporal network is designed in the fault recognition module to perform the fault diagnosis of rotating machinery. The experimental results show that the proposed method can achieve high quality data-level fusion and outperforms the state-of-the-art fault diagnosis methods in terms of fault classification.

Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network

Due to the complex and rugged working environment of real machinery equipment, the resulting fault information is easily submerged by severe noise interference. Additionally, some informative features may be omitted if the feature learning concerns only a single sensor of machinery vibration data. Therefore, to mine more comprehensive fault information and achieve more robust fault diagnosis results, this study proposes a machinery multi-sensor fault diagnosis method based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. As an extension of the feature mode decomposition (FMD), the adaptive multivariate feature mode decomposition (AMFMD) with the improved whale optimization algorithm (IWOA) is firstly presented to automatically decompose the collected multi-sensor vibration data into a group of multichannel mode components, which both inherit the anti-noise robustness of the original FMD and overcome the obstacles of artificial parameter selection of FMD. Subsequently, multichannel mode components containing the most abundant fault information are selected via an impulse sensitive measure hailed as multichannel comprehensive index (MCI), and the frequency slice wavelet transform (FSWT) of the selected multichannel mode components is further calculated and organically fused to generate the colored multichannel time–frequency representation (MTFR) containing multi-sensor important signatures. Finally, by integrating the advantages of feature learning of residual network (ResNet) and convolutional neural network (CNN), a multi-attention fusion residual convolutional neural network (MAFResCNN) with squeeze-excitation module (SEM) and convolutional block attention module (CBAM) is constructed to simultaneously capture global and local feature information from the fused multichannel time–frequency representation and implement automatic discrimination of machinery fault states, which can both enhance machinery fault information and whittle down the useless information, even promote the feature learning performance without significantly increasing the computational burden of the model. The validity of the proposed approach is verified by a diagnosis case of a real wind turbine, demonstrating that the proposed approach has superiority in machinery fault identification compared with some similar techniques.

Multisensor fault diagnosis via Markov chain and Evidence theory

In multi-sensor fusion, the Dempster–Shafer theory is frequently used for fault diagnosis and other decision-making problems. However, if the information collected from various sensors exhibits uncertainty and high conflict, the classical Dempster’s Combination Rule may produce a counter-intuitive result. Various studies were conducted by Ghosh and other scholars to solve this problem, and good results were achieved. This work provides a novel idea which uses the Markov chain to model the uncertainty information in evidence theory, explore the ordered rules in random evidence, and then obtain the evidence support degree, thereby reducing the effect of high-conflict evidence. Considering the information amount of evidence combined with the evidence support degree, the final credibility of evidence is obtained, and the weighted summation is used to obtain new evidence. Finally, the classical Dempster combination rule is used to process new evidence and make the final decision. Through numerical examples and case studies, the proposed method’s superior efficiency and robustness over the existing multisensor fault diagnosis methods are illustrated.

Graph features dynamic fusion learning driven by multi-head attention for large rotating machinery fault diagnosis with multi-sensor data

Recently, rotating machinery fault diagnosis studies based on graph neural networks (GNN) have received some satisfactory achievements. But most of them are based on the analysis of the single sensor signals, which cannot capture the comprehensive fault information, especially aiming at large rotating machineries. A few research using GNN for multi-sensor fault diagnosis only fuse multi-source features in the construction of the input graph, and the fusion effect largely depends on the manual feature selection. Graph attention network (GAT), as an emerging GNN, can give trainable weights to vertices based on the self-attention mechanism to improve the effectiveness of feature learning. And it has not yet been used in the field of multi-sensor fault diagnosis. To fill this gap and utilize GAT’s advantages, this paper presents a multi-sensor multi-head GAT (MMHGAT) model for large rotating machinery fault diagnosis. With the input of several subgraphs, the designed MMHGAT model consisting of two graph attention layers (GAL), a feature fusion process and a Softmax classifier, can dynamically fuse and mine the high-level fault characteristics during the training process. By employing the experiment on the axial flow pump, the effectiveness and superiority of the proposed method are validated.

Ensemble Multiple Distinct ResNet Networks With Channel-Attention Mechanism for Multisensor Fault Diagnosis of Hydraulic Systems

Hydraulic systems are widely used in modern industries, and the study of efficient and accurate fault diagnosis techniques for hydraulic systems can help prevent accidents and reduce economic losses. Most of the existing deep learning-based intelligent diagnosis methods use only single-channel signals for diagnosis, which may ignore some important fault information. In addition, the existing deep learning algorithms cannot distinguish the sensitivity of inter-channel features, and the important features of the channel domain cannot be emphasized. To solve the above problems, this paper proposes a multi-channel data-driven framework integrated distinct ResNet networks with channel-attention mechanism (CM-ResNet) for fault diagnosis of hydraulic systems. Firstly, the multi-channel signals are converted into 2-dimensional feature maps by continuous wavelet transform. Then, multiple CM-ResNet are trained as base-learners based on the signals of each channel. Third, the category probabilities of each base-learner are concatenated into new feature vectors. Finally, the new feature vectors are used to train the meta-learner and perform fault diagnosis. The meta-learner can capture the complex nonlinear relationships between the base-learners and obtain a stronger learner. Experimental results show that the performance of our proposed method outperforms the comparison method and the already existing methods.

Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System

Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault diagnosis method is proposed to solve the problems of difficulty in integrating the fault diagnosis algorithm and locating fault parts due to the complexity of modern mechanical systems. The complexity of modern industrial intelligent systems is due to the fact that the systems are composed of multiple components and there are various connections between them. Common fault diagnosis is to design specialized fault identification algorithms for the physical characteristics of each component, and the integration of different algorithms is a major challenge for system performance. Therefore, this paper investigates a general algorithm for the fault diagnosis of complex systems using the timing characteristics of sensors and transfer entropy. The fault diagnosis algorithm is based on the prediction of multi-dimensional long time series using Autoformer, and fault identification is performed based on the deviation of the predicted value from the actual value. After fault identification, a root cause analysis method of faults based on transfer entropy is proposed. The method can locate the component where the fault occurs more accurately based on the analysis of the cause-effect relationship of each component and help maintenance personnel to troubleshoot the fault.

AttGGCN Model: A Novel Multi-Sensor Fault Diagnosis Method for High-Speed Train Bogie

The bogie system is a critical system for a high-speed train, which is composed of various mechanical parts. Therefore, the health of the bogie can directly affect the health of high-speed train. Temperature signals, speed signals and pressure signals are collected from the bogie can reflect its health. Hence, the multi-sensor fault diagnosis methods can provide novel solutions for the bogie health monitoring tool. This paper presents a novel bogie fault diagnosis scheme named the AttGGCN model, using graph convolutional network (GCN), gated recurrent unit (GRU) and attention mechanism. In this fault diagnosis scheme, the bogie data network is established firstly. Then, temporal and spatial features are extracted and fused using GCG unit. Finally, the GCN are used for fault identification. Twenty-four kinds of measured signals and seven types of faults from actual High-speed train in operation are utilized for verification. Results show that the AttGGCN model has the highest accuracy compared to conventional models. In addition, experiments on different scales of training sets suggest that the AttGGCN model has strong robustness in small-scale datasets. Besides, ablation experiments certificate that the attention mechanism is able to strengthen the feature extraction ability.

Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis

To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning.

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

DWT-LSTM-Based Fault Diagnosis of Rolling Bearings with Multi-Sensors

Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component of rotating machinery, the failure of rolling bearings may lead to serious accidents during the industrial production operation. In order to accurately diagnose the fault status of rolling bearings, a novel long short-term memory (LSTM) model with discrete wavelet transformation (DWT) for multi-sensor fault diagnosis is proposed in this paper. The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies hidden in the time series of the fault information. The proposed DWT-LSTM method makes full use of the advantages of feature extraction based on expert experience and deep network learning to discover complex patterns from a large amount of data. Finally, the feasibility and efficiency of the proposed method are illustrated by comparison with the existing methods.

IDCR: Improved Dempster Combination Rule for multisensor fault diagnosis

Data gathered from multiple sensors can be effectively fused for accurate monitoring of many engineering applications. In the last few years, one of the most sought after applications for multisensor fusion has been fault diagnosis. Dempster–Shafer Theory of Evidence along with Dempster’s Combination Rule is a very popular method for multisensor fusion which can be successfully applied to fault diagnosis. But if the information obtained from the different sensors shows high conflict, the classical Dempster’s Combination Rule may produce counter-intuitive result. To overcome this shortcoming, this paper proposes an improved combination rule for multisensor data fusion. Numerical examples have been put forward to show the effectiveness of the proposed method. Comparative analysis has also been carried out with existing methods to show the superiority of the proposed method in multisensor fault diagnosis.

Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

Due to the use of multiple observers and controllers in multi-sensor fault-tolerant control of PMSM drive systems, the algorithm is complex and the system control performance is affected. In view of this, the paper studies multi-sensor fault diagnosis and active fault-tolerant control strategies based on a composite sliding mode observer. With the mathematical model of PMSM built, a design method of the composite sliding mode observer is proposed. A single observer is used to observe and estimate various state variables in the system in real time, which simplifies the implementation of observer-related algorithms. In order to improve the diagnostic accuracy of different types of sensors under different faults, a method for determining fault thresholds is proposed through global search for the maximum residual value. Based on this, a fault diagnosis and active fault-tolerant control strategy is proposed to realize fast switching and reconstruction of feedback signals of closed-loop control systems under different faults of multiple sensors, thus restoring the system performance. Finally, the effectiveness of the proposed algorithm and control strategy is verified by simulation experiments

Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

Fault diagnosis is a typical multisensor information fusion problem. The information obtained from different sensors, such as sound, pressure, vibration, and temperature, can be considered as a piece of evidence. From the viewpoint of the evidence theory, the problem of multisensor fault diagnosis can be viewed as the problem of evidence fusion and decision. However, the information obtained from different sensors may be inaccurate, uncertain, fuzzy, or even conflict, so how to set up the fault diagnosis architecture of a distributed multisensor system and combine the conflict evidence should be taken into consideration. In this paper, the classical Dempster–Shafer evidence theory is described and the disadvantage of a classical Dempster's combination rule is discussed. In order to solve the counter-intuitive result when using the classical Dempster's combination rule, the Euclidean distance is proposed to characterize the differences between different pieces of evidence, and then the support degree of each evidence is generated and the weighted pieces of evidence can be combined directly using the classical Dempster's combination rule. Numerical simulation examples indicate that the proposed method has a better performance of analyzing the conflict between different pieces of evidence, especially for high conflict evidence. Therefore, compared with the existing methods, it has better applicability. According to the requirement of the Dempster–Shafer evidence theory, the fault diagnosis architecture of a distributed multisensor system is analyzed in detail, and a fault case of a rotating machine is used to illustrate that the proposed model is effective and superior, which can be used in practice.

Multisensor fault diagnosis technology based on convex optimization theory

Multisensor fault diagnosis technology based on convex optimization theory