Mechanical Fault Diagnosis Method Research Articles

Domain graph attention neural network: A new mechanical fault diagnosis method with few samples

Mechanical fault diagnosis is currently a highly trending topic, facing two significant challenges. Firstly, the acquisition of an ample number of fault samples proves to be difficult, thereby limiting access to sufficient data samples. Secondly, intricate and non-mathematically describable associations often exist among different faults. Most algorithms treat fault samples as isolated entities, consequently impacting the accuracy of fault diagnosis. This paper proposes a novel machine learning framework called Domain Graph Attention Neural Network (DGAT), which leverages the topological structure of graphs to effectively capture the interrelationships among fault samples. Additionally, this framework incorporates domain information during node updates to obtain richer embeddings, particularly in scenarios with limited available samples. It effectively overcomes the fixed receptive field limitation of the original Graph Attention Network (GAT). In order to validate the effectiveness of the model, we conducted extensive comparative experiments on diverse datasets, which demonstrated the superior performance of the proposed model.

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.

Research on mechanical fault diagnosis method of high voltage circuit breaker based on new measurement

Aiming at the problem that the detection of a single signal is difficult to accurately and comprehensively reflect the mechanical failure of the circuit breaker operating mechanism, the VS1 (VBM7) -12 high-voltage circuit breaker is selected as the experimental object in this paper. The coil current signal is collected by a sensor based on new measurements, and the fault diagnosis method of high-voltage circuit breaker is studied from the acquisition, preprocessing, and feature extraction of vibration signal and sound signal, combined with artificial intelligence algorithm. The research shows that the application of electro-acoustic joint features can effectively enhance the precision of fault diagnosis of high-voltage circuit breakers, and verify the feasibility and superiority of the mechanical fault diagnosis method of high-voltage circuit breakers based on new measurements.

Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

The sensor signals with multiple measuring points and data types not only bring sufficient information, but also put forward more stringent requirements for multi-sensor fusion efficiency and fault feature extraction capability. The redundancy and conflicts in the information of multi-sensor signals often hinder the accurate extraction of crucial fault features. To address this problem, our study proposes an intelligent mechanical fault diagnosis method, which is based on a multi-head spatio-temporal attention mechanism and parallel gated recurrent units (GRUs) architecture. This method utilizes multiple attention heads to model the correlation information in spatial and temporal dimensions, and employs a parallel GRU network for targeted feature extraction. Finally, it combines local features from different attention heads to achieve flexible scheduling of various spatio-temporal attention modes. This novel application and fusion approach of multi-head attention enables accurate identification of the spatio-temporal value differences in the collected multi-sensor signals from multiple perspectives. Experimental results on multiple mechanical fault datasets show that the proposed method performs well in multi-sensor signals based mechanical fault diagnosis tasks and can maintain effectiveness under small samples and imbalanced data conditions.

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. The 110 kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

Transformer winding mechanical fault diagnosis method based on closing transient acoustic vibration signal

The current conventional transformer winding fault diagnosis method mainly extracts the vibration signal through the time-frequency analysis method, which leads to poor diagnosis due to the lack of analysis of the transformer winding state. In this regard, a transformer winding mechanical fault diagnosis method based on the closing transient acoustic vibration signal is proposed. The closing vibration signals of the transformer in the normal and loose winding states are collected separately, the transformer winding force situation is analysed, and the transformer fault signal characteristics are extracted and combined with a fault classifier to construct a fault diagnosis model. In the experiments, the proposed method is verified for fault identification. The experimental results show that the proposed method has a high correct rate when used to identify different types of transformer faults, and has a more desirable fault diagnosis performance.

Research on Mechanical Equipment Fault Diagnosis Method Based on Deep Learning and Information Fusion.

Solving the problem of the transmission of mechanical equipment is complicated, and the interconnection between equipment components in a complex industrial environment can easily lead to faults. A multi-scale-sensor information fusion method is proposed, overcoming the shortcomings of fault diagnosis methods based on the analysis of one signal, in terms of diagnosis accuracy and efficiency. First, different sizes of convolution kernels are applied to extract multi-scale features from original signals using a multi-scale one-dimensional convolutional neural network (1DCNN); this not only improves the learning ability of the features but also enables the fine characterization of the features. Then, using Dempster-Shafer (DS) evidence theory, improved by multi-sensor information fusion strategy, the feature signals extracted by the multi-scale 1DCNN are fused to realize the fault detection and location. Finally, the experimental results of fault detection on a flash furnace show that the accuracy of the proposed method is more than 99.65% and has better fault diagnosis, which proves the feasibility and effectiveness of the proposed method.

A new mechanical compound fault diagnosis method based on morphological filter and RLS

Mechanical compound fault diagnosis is a thorny issue in the industry. To overcome this problem, a method named morphological filter—recursive least squares (MF-RLS) is proposed in this paper. In the proposed method, MF-RLS is used to sequentially separate the different fault impulse features by decomposing the observed signal into a series of iterative morphological filtering components (IMFCs). First, the measured signal is decomposed into different scales by the multi-scale MF. Then, the product of kurtosis and envelope harmonic-to-noise ratio index is used to select the best IMFC. Finally, the IMFC is input to RLS to separate other fault features. After continuous iterations, the separation and extraction of the compound fault impulse features are achieved. The simulation and experiment of the mechanical compound fault have verified the effectiveness of the proposed method.

Deep auto-encoder network for mechanical fault diagnosis of high-voltage circuit breaker operating mechanism

Abstract To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and fully exploit the characteristic information in the vibration signal of the high-voltage circuit breaker, a mechanical fault diagnosis method of the operating mechanism of the high-voltage circuit breaker based on the deep self-encoding network is proposed. First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time–frequency subplane of the vibration signal is then calculated, and the time–frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126 kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.

A Dense ResNet Model with RGB Input Mapping for Cross-Domain Mechanical Fault Diagnosis

In actual engineering applications, the mechanical machine is exposed to uncertain conditions such as noise interference and various loads. The commonly used fault diagnosis models suffer degradation in the prediction accuracy in such complex industrial environments where the available label samples are insufficient and the conditions are varied. To combat this challenge, a cross-domain mechanical fault diagnosis method based on the deep-learning networks is proposed. It utilizes small samples, i.e., 10% of the total, and operates on the time-series signal collected from the mechanical equipment. It provides a classification accuracy of more than 97% on the dataset from Case Western Reserve University (CWRU) under variable conditions and 97.56% with the noise interference of 0 dB. The one-dimensional vibration signal is first converted into an image through RGB mapping. Then, the derived RGB image is capable of the time dependent and spatial properties of the time sequence signal and can be directly used as the input of the deep-learning networks. The deep-learning networks model, i.e., the ResNet, is adopted for the fault feature extraction and additional dense connections are added among the residual blocks to supplement the insufficient labeled samples within the networks. Then, an RGB-DResNet is constructed, capable of retaining the robust features for the classification of the mechanical faults in different working conditions. Finally, through retraining the model by use of transfer learning, the derived RGB-TDResNet model gives a fine adaption to the feature distribution with a small amount of target domain information. The performance of the proposed fault diagnosis model was validated on the dataset from CWRU. The results show that it provides a high identification accuracy and strong robustness in variable operating conditions as well as the noise environment. It is a rather promising approach for dealing with the cross-domain tasks of mechanical fault diagnosis.

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

A mechanical part fault diagnosis method based on improved multiscale weighted permutation entropy and multiclass LSTSVM

Multi-scale permutation entropy (MPE) has poor stability, accuracy and noise immunity, recognition accuracy of twin SVM is low, so a diagnosis method based on improved multi-scale weighted permutation entropy (IMWPE) and least squares TSVM (LSTSVM) is proposed. The innovations include that the improved variational mode decomposition (IVMD) based on energy and mutual information is used to suppress noise, and novel composite coarse-grained and permutation pattern weighting methods are introduced to construct IMWPE, after which the noise immunity, stability and accuracy of features have been improved. LSTSVM is extended to a multi-class LSTSVM through one vs one (OVO) and binary tree (BT) strategies, the performance of the proposed methodology is significantly improved. The results show that signal-to-noise ratio of signal is enhanced, the stability of IMWPE is improved, the diagnosis accuracies for bearing and check valve are increased by 17.084 % and 12.498 %, which provides a reference for fault diagnosis under noise.

A Novel Data-Driven Mechanical Fault Diagnosis Method for Induction Motors Using Stator Current Signals

Most of the mechanical fault diagnosis methods of induction motors (IMs) are based on vibration signals. However, vibration sensors are expensive and require direct contact with IMs. This article proposes a data-driven mechanical fault diagnosis method for IMs using stator current signals, which is more convenient and low cost, since no additional sensors are required. Aiming at the weak representation of mechanical faults in current signals, an intelligent noise elimination method based on noise reconstruction model is proposed to improve the signal-to-noise ratio. Through the automatic feature extraction and classification of the residual current envelope spectrum, high diagnosis accuracy can be obtained even if some differences have existed among samples. The effectiveness of the proposed method is verified by high accuracy diagnosis results on two experimental platforms. The results show that the average diagnostic accuracy of the proposed algorithm for one bearing fault and two eccentricity faults can reach 96%. Even if the fault type and the fault degree are distinguished at the same time, an accuracy of 90% can be achieved for six kinds of bearing faults.

Research on an intelligent diagnosis method of mechanical faults for small sample data sets

The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

An intelligent index-driven multiwavelet feature extraction method for mechanical fault diagnosis

To sufficiently perform the inner product match principle of mechanical fault diagnosis, the construction and selection of the basic functions are the critical issues. Hereinto, the construction of basic functions increases the ability of accurate inner product match, but the selection determines the final matching accuracy for fault diagnosis. Thus, an intelligent index-driven multiwavelet feature extraction method is proposed for mechanical fault diagnosis, which is essentially an accurate inner product matching established by the ‘appropriate’ multiwavelet basic functions guiding by an intelligent data-driven index. First, an intelligent index by the improved weighted square envelope spectrum is designed for the data-adapted selection guidance. Hereinto, the optimal weights of purified envelope spectrum by multiwavelet neighboring coefficient denoising are searched by support vector machine. Second, a basic function library is established by two excellent families of SA4-type and Hermite lifting-based multiwavelets. Third, guiding by the maximization of the intelligent index, the optimal basic functions are selected from the candidate multiwavelet library and employed to extract the fault features. Finally, two experimental case studies of bearing degradation data verify the effectiveness and feasibility of this method with comparisons. The results show that it could extract the fault features at different stages, especially at the very initial stages, offering a useful tool for mechanical fault diagnosis.

Review on mechano-electronic device fault diagnosis based on Hilbert-Huang Transformation

This paper reviews the new Hilbert-Huang Transformation (HHT for short) signal processing method, briefly discusses the significance of time-frequency analysis methods in electromechanical system fault diagnosis, points out the superiority of HHT for fault diagnosis with respect to the characteristics of nonstationary and nonlinear dynamic fault signals, and introduces the basic principles of HHT. The research applications of HHT in the field of fault diagnosis at home and abroad are discussed, showing that HHT is becoming an important signal processing method for mechanical fault diagnosis.

Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

In order to solve the problem that a large number of vibration signals cannot be transmitted in real time in the application of wireless sensor networks (WSNs) in mechanical fault diagnosis, a mechanical fault diagnosis method based on multilevel and hierarchical information fusion of WSNs was proposed. In this method, the cluster tree network structure is used to expand the coverage of network monitoring, and WSNs information fusion is divided into three levels: data-level fusion, feature-level fusion, and decision-level fusion. The terminal node performs data-level fusion on the original vibration information to extract feature information; the cluster-head node performs feature-level fusion on the feature information to obtain pattern recognition results; and the gateway node performs decision-level fusion on the recognition results to evaluate the running status of mechanical equipment. The results show that the slight damage fault of the bearing inner ring can be accurately diagnosed by decision-level fusion based on four groups of probability distribution functions. According to the statistics of 30 test results, the fault recognition rate is 83.3%. The method can be applied to mechanical fault diagnosis effectively.

Feature-guided regularization parameter selection in sparse de-noising for fault diagnosis

Using the sparse and redundant representation model to de-noise vibration signals is an effective method for mechanical fault diagnosis. The selection of the regularization parameter in the sparse de-noising algorithm plays a vital role in reconstructing fault features. The current choices, however, are primarily based on fixed-form parameters derived under the assumption of additive white noise or based on the “trial and error” method. As a result, the extracted de-noised signals often fail to show fault features. To solve this difficulty, a self-adaptive regularization parameter selection strategy is proposed, which directly measures the significance of fault features in sparse de-noised signals. Firstly, the entire solution path is obtained by the computationally efficient least angle regression (LARS) algorithm. Besides, a feature presence probability index (FP) is well designed to measure the significance of fault features in the reconstructed signal envelope demodulation spectrum. Accordingly, with the help of the maximal FP indicator, the optimal regularization parameter and LARS solution is determined. The proposed method is not limited by noise probability distributions; therefore, it shows the superiority of extracting fault features in both the simulation and experimental data analysis.

Comprehensive detection method for multi-contaminants in hydraulic oil based on inductance-resistance-capacitance analysis

Oil condition monitoring technology is a practical method for mechanical fault diagnosis, because oil contamination can directly reflect the lubrication and wear state. A comprehensive detection method based on integrated sensor consisting of an inductance-resistance sensing unit and a capacitance sensing unit is introduced in this article. Inductance parameter differentiates metallic debris, and the coil-based resistance parameter can detect smaller non-ferromagnetic metallic debris. Capacitance parameter is used to measure air bubbles and moisture, and the moisture measurement is not interfered by solid impurities. The simulation analysis showed that annular channel reduces the instability of inductive-resistive detection caused by position effect. The experimental results proved that the integrated inductive-resistive-capacitive sensor detects air bubbles, moisture, ferromagnetic debris and non-ferromagnetic debris by comparing multi-parameter signals. This method expands the detection range for oil contamination, and reduces the probability of incorrect particle counting and incorrect size measurement, which provides technical support for the intelligent operation and maintenance of mechanical equipment.

A novel assessable data augmentation method for mechanical fault diagnosis under noisy labels

Data augmentation technology has achieved great success to expand the training set for several years. As a representative technology, generative adversarial network and its variants are widely applied in many data augmentation tasks. But the quality of training samples is rarely considered. In this paper, a novel assessable data augmentation named ADA is proposed for mechanical fault diagnosis under noisy labels. First, a sample quality assessment procedure including assessment model construction, approximate calculation based on influence function and screening decision is presented. Thereby, the optimized training set can be obtained. Then, the WGAN-gp model can be established based on the optimized training set and the data augmentation can be accomplished. Finally, a classifier can be trained with the expanded training set and achieve the task of fault diagnosis. The results of two experiments show that the proposed ADA method can effectively improve the fault diagnosis accuracy for various classifiers.