Fault Diagnosis Of Mechanical Equipment Research Articles

Auxiliary generative mutual adversarial networks for class-imbalanced fault diagnosis under small samples

The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is often premised on big data and class-balance. However, due to the limitation of working environment, operating conditions and equipment status, the fault data collected by mechanical equipment are often small and imbalanced with normal samples. Therefore, in order to solve the above-mentioned dilemma faced by the fault diagnosis of practical mechanical equipment, an auxiliary generative mutual adversarial network (AGMAN) is proposed. Firstly, the generator combined with the auto-encoder (AE) constructs the decoder reconstruction feature loss to assist it to complete the accurate mapping between noise distribution and real data distribution, generate high-quality fake samples, supplement the imbalanced dataset to improve the accuracy of small sample class-imbalanced fault diagnosis. Secondly, the discriminator introduces a structure with unshared dual discriminators. Realize the mutual adversarial between the dual discriminator by setting the scoring criteria that the dual discriminator are completely opposite to the real and fake samples, thus improving the quality and diversity of generated samples to avoid mode collapse. Finally, the auxiliary generator and the dual discriminator are updated alternately. The auxiliary generator can generate fake samples that deceive both discriminators at the same time. Meanwhile, the dual discriminator cannot give correct scores to the real and fake samples according to their respective scoring criteria, so as to achieve Nash equilibrium. Using three different test-bed datasets for verification, the experimental results show that the proposed method can explicitly generate high-quality fake samples, which greatly improves the accuracy of class-unbalanced fault diagnosis under small sample, especially when it is extremely imbalanced, after using this method to supplement fake samples, the fault diagnosis accuracy of DCNN and SAE are relatively big improvements. So, the proposed method provides an effective solution for small sample class-unbalanced fault diagnosis.

A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.

A Fault Identification Method of Mechanical Element Action Unit Based on CWT-2DCNN

Aiming at the problems of low recognition rate and human intervention in the traditional fault diagnosis of mechanical equipment, a fault identification method based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (2DCNN) is proposed. By collecting the vibration signals of four kinds of fault states and normal states of the worm rotation unit of the CNC machine tool, the data are preprocessed and identified. Firstly, the vibration signals of each fault state of the element action unit are CWT transformed into the corresponding two-dimensional time-frequency diagram; then, the 2DCNN fault identification model is established, and the time-frequency diagrams of various faults are input to the network as characteristic diagrams for training and testing. Through the adjustment of network parameters, the network performance is gradually optimized; finally, the hybrid domain attention module CBAM is added to further improve the network structure, and the recognition effect is compared with the initial 2DCNN. The results show that the CWT-2DCNN meta-action unit fault recognition model with an attention module can recognize the different states of meta-action units more effectively, and the fault recognition effect is better. By using this method, the different fault types of mechanical element action units can be accurately identified, which has a certain application in the field of mechanical fault identification and diagnosis.

Remote Control and Fault Diagnosis of Port Mechanical Equipment Based on Wireless Communication Technology

In order to solve the problem that there is a huge amount of port machinery operating state data, the traditional fault diagnosis method is difficult to effectively obtain the mechanical fault information from the massive data and also cannot accurately diagnose the fault. This paper presents a fault diagnosis method for port machinery based on the FastAP algorithm. By analyzing the characteristics of the FastAP algorithm, the algorithm is introduced into port machinery fault diagnosis, and a port machinery fault diagnosis model based on FastAP is proposed. Through simulation experiments, the algorithm and its application in port machinery fault diagnosis are verified. The experimental results show that compared with the standard AP algorithm, the FastAP algorithm performs well in the sum of similarity, clustering accuracy, and clustering calculation time and has better performance, which is more suitable for fault diagnosis. The fault diagnosis method based on the FastAP algorithm has a high diagnosis accuracy of port machinery fault, reaching more than 92%, and can accurately diagnose the normal state, inner and outer ring fault state, and rolling element fault state of the actual port mechanical belt conveyor. The research on remote control and fault diagnosis of port machinery equipment based on wireless communication technology can effectively solve the problem that there is a huge amount of port machinery operating state data, and the traditional fault diagnosis methods are difficult to effectively obtain mechanical fault information from the massive data and thus cannot accurately diagnose the fault.

Study on the Sensitivity of Detachable Wear Particle Sensor Based on Iron-Based Amorphous Soft Magnetic Rings

A parallel three-coil wear particle sensor is innovatively proposed with the detachable substrate and magnetic rings. The magnetic rings are designed and fabricated using polydimethylsiloxane (PDMS) and iron-based amorphous soft magnetic material (IASM). The excitation coils surround the magnetic rings, leaving only a 1mm air gap on the inner side (near the flow channel). The magnetic rings are magnetized under the action of the high-frequency alternating magnetic field, which forms an enhanced magnetic field with a high gradient distribution in the detection area. Through numerical simulation and experimental verification, the influence of IASM concentration, radial position and size of wear particles on the sensor output signal are studied. With the increase of IASM concentration, the sensor detection sensitivity rises dramatically. The sensor with 75&#x0025; IASM magnetic rings can effectively detect 60 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> iron particles and 160 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> copper particles in a 10mm flow channel; the detection error rate is less than 15&#x0025; in the actual lubrication circuit, which fully meets the needs of early abnormal wear fault diagnosis of mechanical equipment. Therefore, this method is a promising candidate for developing the large-flow electromagnetic wear particle sensor sensitivity.

Research on online monitoring and fault diagnosis system based on multivariate empirical mode decomposition

Abstract Mechanical equipment is crucial to industrial production, so the monitoring and fault diagnosis of its vibration signals is important to ensure production safety. And this paper mainly researches on an online monitoring and fault diagnosis system. The system makes use of multiple sensors to collect vibration signals from different positions of machines for multivariate signals so that condition of the machines can be monitored and information of local faults can avoid loss. Besides, this paper applies a method for fault feature extraction of mechanical equipment based on multivariate empirical mode decomposition (MEMD), which can accurately extract frequency of fault features to realize fault diagnosis when the features are weak in the early period. And this paper puts forward a design to the overall framework of the system based on technical requirements of the system, then introduces the multi-sensor mechanical fault diagnosis method based on MEMD. On such basis, this paper employs LabVIEW and Python to develop the upper computer software of the system, and experiments are carried out to testify whether this system is viable or not. The research results show that the system developed by this paper has practical application value in condition monitoring and fault diagnosis of mechanical equipment.

A Novel Generative Method for Machine Fault Diagnosis

Deep learning is widely used in fault diagnosis of mechanical equipment and has achieved good results. However, these deep learning models require a large number of labeled samples for training, which is difficult to obtain enough labeled samples in the actual production process. However, it is easier to obtain unlabeled samples in the industrial environment. To overcome this problem, this paper proposes a novel method to generative enough label samples for training deep learning models. Unlike the generative adversarial networks, which required complex computing time, the calculation of the proposed novel generative method is simple and effective. First, we calculate the Euclidean distance between the training sample and the test sample; then, the weight coefficient between the training sample and the test sample is settled to generate pseudosamples; finally, combine with the pseudosamples, the deep learning method is training for machine fault diagnosis. In order to verify the effectiveness of the proposed method, two experiment datasets with planetary gearboxes and wind gearboxes are carried out with different activation functions. Experimental results show that the proposed method is effective for most activation function models.

A Double Lock-in Amplifier Circuit for Complex Domain Signal Detection of Particles in Oil

The detection of metal particles in oil is of great significance to the fault diagnosis of mechanical equipment. Real domain detection methods cannot simultaneously identify the sizes and materials of particles. Because a metal particle in an alternating magnetic field induces a complex domain signal whose strength relates to the particle properties, this paper introduces a double lock-in amplifier circuit (DLAC) into a triple-coil inductive particle sensor to detect the complex domain signal under noise interference conditions. Furthermore, various sizes of red copper, brass, aluminum, iron, and type 403 steel particles are chosen as the experimental materials. For the first time, the complex domain signals induced by the five kinds of particles are detected. Hence, in the complex plane, a method is proposed to identify the size and material of a particle based on the amplitude and phase of these signals. With the high sensitivity and strong antinoise ability of the DLAC, this system can detect 25 μm iron particles and 70 μm copper particles. This study presents a new strategy for the online detection of particles in oil.

Iterative Error Self-Correction for Robust Fault Diagnosis of Mechanical Equipment With Noisy Label

Deep neural network (DNN) is an effective technology for machinery fault diagnosis. The good performance of DNN is based on the assumption that all labels are completely correct. However, mislabeled data is common in actual industrial applications, which will cause severe performance degradation. This paper explores the performance of DNN under noisy labels and the reasons for its performance degradation. Furthermore, a novel iterative error self-correction (IESC) algorithm based on the maximum-activation of softmax is proposed. During the training process, the label is dynamically optimized, and it is no longer fixed. IESC automatically models the distribution of correct labels, gradually identifies incorrect labels, and automatically corrects incorrect labels. In addition, a noise-tolerant loss is introduced to enhance the network&#x2019;s noise robustness. Experiments on two real machinery fault diagnosis cases prove that our method has excellent label correction and fault diagnosis performance. It significantly improves the performance of DNNs and promotes its practical application potential.

Convolutional Neural Network Based Two-Layer Transfer Learning for Bearing Fault Diagnosis

Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was created to learn in the target domain by accessing knowledge from the pertinent labelled source domain. Inspired by Maximum Mean Discrepancy, this paper puts forward a Convolutional Neural Network (CNN) based Two-layer Transfer Learning (CTTL) method for fault diagnosis. In the first layer, the fault features are automatically extracted by CNN and a term called Feature Weighted Maximum Mean Discrepancy (WMMD) is considered to minimise the difference between source and target domains. In the second layer, the Third Dataset, which is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method was designed on the basis of the predicted labels from the first layer. The Calinski-Harabaz (CH) index of the Target Dataset controls the iteration times of CTTL. CTTL change the process of Transfer Learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy. Proposed CTTL is tested by the bearing datasets of Case Western Reserve University (CWRU) and XJTU-SY. The experimental findings reveal that CTTL is capable of achieving a high diagnosis accuracy across different load domains. In the majority of experiments, CTTL outperformed other algorithms, including Deep Neural Network, Support Vector Machine, and several other methods.

Depth Prototype Clustering Method Based on Unsupervised Field Alignment for Bearing Fault Identification of Mechanical Equipment

In the process of mechanical equipment fault diagnosis, it is difficult to obtain enough labeled samples due to the changeable operating conditions, complex working environment, and the limitation of measuring equipment. Therefore, a prototype clustering method based on unsupervised domain alignment is proposed for fault detection and diagnosis of mechanical equipment under unmarked samples. First, the relative maximum mean discrepancy index is constructed to map the data collected from different devices to the same high-dimensional space for feature alignment. Then, cluster the output and calculate the prototype for each category. Compared with the classification directly using the softmax function, the prototype clustering method can correct the local possible misjudged points through the overall average distribution. By minimizing the global distance error, prototypes obtained by clustering in the target domain are matched with prototypes of tagged samples in the source domain and, finally, realize unsupervised state recognition. Six experiments are designed to verify the effectiveness of the proposed method. The accuracy of the proposed method is 1&#x0025;&#x2013;2&#x0025; higher even when compared to one of the most advanced fault diagnosis methods available. Also, it can be found from the results of t-stochastic neighbor embedding (SNE) visualization that the proposed method has higher knowledge transfer capability.

Machinery New Emerge Fault Diagnosis Based on Deep Convolution Variational Autoencoder and Adaptive Label Propagation

In the research field of mechanical equipment fault diagnosis, usually only the existing fault types are identified, and the new emerge class of the fault is ignored, however, the new emerge fault class may also occur actually. In order to solve the problem, a novel fault diagnosis model based on deep convolution variational autoencoder network and adaptive label propagation (DCVAN-ALP) is proposed. Firstly, the initial high dimensional features are constructed by using the double tree complex wavelet packet method as the input of the model. Secondly, the convolutional neural network architecture is applied to construct the variational autoencoder, and the local and non-local characteristics of samples are embedded into the loss function for training, which is considered to improve the identification of hidden layer features of the neural network. Finally, t-SNE and the improved label propagation algorithm are adopted to process the hidden features of the neural network, which can achieve the purpose of diagnosing the existing fault class and especially new emerge fault class. Experimental results show that the proposed model can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers in diagnosing new emerge fault class.

Application of Computer Algorithm in Fault Diagnosis System of RM Equipment

At present, modern mechanical equipment is gradually developing towards large-scale and intelligent, which leads to more and more complex equipment structure. Therefore, people have higher and higher requirements for intelligent fault diagnosis of mechanical equipment, which leads to the application of various algorithms to mechanical equipment. Among them, rotating machinery (hereinafter referred to as RM) mainly relies on rotating action to complete specific functions, such as gearbox, gas turbine, generator and engine, which are often the core components of mechanical equipment. Therefore, the FSGS (hereinafter referred to as FSGS) of RM equipment has become a very key link in system design and maintenance, which requires designers to constantly overcome the original intelligent diagnosis system. Through a variety of deep learning algorithms, we can improve the diagnosis efficiency of automatic monitoring and diagnosis equipment, which can also reduce the loss caused by untimely diagnosis. Firstly, this paper analyzes the types of application of computer algorithms in the fault body segment system of RM equipment. Then, this paper analyzes an algorithm, which can better improve the diagnosis efficiency of the equipment.

Exploiting graph neural network with one-shot learning for fault diagnosis of mechanical equipment

For the classification of mechanical fault diagnosis, a graph neural network (GNN) method with one-shot learning is proposed. Convolutional Neural Network (CNN) is used to extract the feature vectors and One-Hot coding from images of Fault diagnosis of mechanical equipment. Inputting feature vectors and One-Hot coding into GNN, according to the Adjacency Matrix between vertices in the Graph, and is used for classification and inference. The method with one-shot learning is used for fault diagnosis classification. Through the fault classification for the industrial robot RV reducer and public data set CWRU pictures, the effectiveness of the method is verified. Five categories are used for fault diagnosis and classification in RV Reducer of the industrial robots. 80 categories are used in the public data set CWRU, and 55 categories are used as the training set. GNN is employed to spread the label information from the supervised sample of the unlabeled query data. The large-scale dataset can then be used as baseline classes to learn transferable knowledge for classifying novelties with one-shot samples. The one-shot learning with graph neural network GNN significantly improves the classification accuracy. The results show that the proposed method is superior to other similar methods and has a substantial potential for improvement in Fault diagnosis of mechanical equipment.

Rolling bearing fault diagnosis based on wireless sensor network data fusion

With the continuous development of intelligent manufacturing, mechanical equipment is developing in the direction of large-scale, integration, precision and intelligence. The coupling between different equipment in the operation of the system also makes the mechanical equipment increasingly become a whole, which puts forward higher requirements for the fault diagnosis of mechanical equipment, especially rolling bearings. With the rapid development of wireless network technology, rolling bearing fault diagnosis has become a reality. In this paper, the wireless sensor network will be innovatively used to collect the data of key parts of mechanical equipment, so as to improve the problem of insufficient and accurate collected data in the traditional convolution neural network fault detection algorithm, convert the corresponding time-domain signal into image signal, and combine the global average pool layer on the basis of the combination of nonlinear convolution layers, So as to optimize the corresponding wireless network structure, and finally realize the adaptive extraction and fault diagnosis of rolling bearing fault features. The experimental results show that the fault detection accuracy of the optimized convolution neural network algorithm is about 99.8%, and has good robustness and practical value.

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base (BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base (C-BRB) model based on Directed Acyclic Graph (DAG) structure is proposed in this paper. By dispersing numerous attributes into the parallel structure composed of different sub-BRBs, C-BRB can effectively reduce the amount of calculation with acceptable result. At the same time, a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable sub-models. Finally, a fusion method based on Evidential Reasoning (ER) rule is used to combine the belief rules of C-BRB and generate the final results. To illustrate the effectiveness and reliability of the proposed method, a case study of fault diagnosis of rolling bearing is conducted, and the result is compared with other methods.

Neural Network Optimization and Data Fusion Recognition Method for Intelligent Mechanical Fault Diagnosis

With the improvement of mechanical equipment complexity and automation level, the importance of mechanical equipment fault diagnosis is more and more prominent, and the choice of appropriate diagnosis method is crucial to the accuracy of the diagnosis results. Wavelet analysis and neural network technology, as the hot spot and frontier of research, are also important research contents in the development of intelligent diagnosis of mechanical fault. Data fusion can process multisource information to obtain more accurate and reliable methods. At the same time, because of its good nonlinearity, adaptability, and fault tolerance, neural network has become the preferred method of mechanical fault diagnosis. This paper first describes the research content and significance of fault diagnosis technology and introduces the main methods and steps of fault diagnosis, and through the introduction of mechanical fault vibration signals, vibration signals were analyzed in time domain and frequency domain. Secondly, the definition and classification of data I fusion and RBF neural network are introduced in detail and compared with BP neural network. Because the prediction accuracy of the RBF network is higher than that of the BP neural network and the training time of the RBF network is obviously shorter than that of the BP network, the RBF network has significant advantages over diagnostic errors. In this paper, six valve signals were collected under normal conditions and errors, and by analyzing and comparing different theoretical foundations, the 4-second network crisis time was effectively reduced, which provided the basis for teaching monitoring.

A Non-Contact Fault Diagnosis Method for Bearings and Gears Based on Generalized Matrix Norm Sparse Filtering.

Fault diagnosis of mechanical equipment is mainly based on the contact measurement and analysis of vibration signals. In some special working conditions, the non-contact fault diagnosis method represented by the measurement of acoustic signals can make up for the lack of contact testing. However, its engineering application value is greatly restricted due to the low signal-to-noise ratio (SNR) of the acoustic signal. To solve this deficiency, a novel fault diagnosis method based on the generalized matrix norm sparse filtering (GMNSF) is proposed in this paper. Specially, the generalized matrix norm is introduced into the sparse filtering to seek the optimal sparse feature distribution to overcome the defect of low SNR of acoustic signals. Firstly, the collected acoustic signals are randomly overlapped to form the sample fragment data set. Then, three constraints are imposed on the multi-period data set by the GMNSF model to extract the sparse features in the sample. Finally, softmax is used to as a classifier to categorize different fault types. The diagnostic performance of the proposed method is verified by the bearing and planetary gear datasets. Results show that the GMNSF model has good feature extraction ability performance and anti-noise ability than other traditional methods.

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine

In recent years, autoencoder has been widely used for the fault diagnosis of mechanical equipment because of its excellent performance in feature extraction and dimension reduction; however, the original autoencoder only has limited feature extraction ability due to the lack of label information. To solve this issue, this study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis. Compared with the existing methods, FD-SAE has stronger feature extraction ability and faster network convergence speed. By analyzing the characteristics of original rolling bearing data, it is found that there are evident differences between normal data and faulty data. Therefore, a simple linear support vector machine (SVM) is used to classify normal data and faulty data, and then the proposed FD-SAE is used for fault classification. The novel combination of SVM and FD-SAE has simple structure and little computational complexity. Finally, the proposed method is verified on the rolling bearing data set of Case Western Reserve University (CWRU).

An Ultrasensitive Debris Microsensor for Oil Health Monitoring Based on Resistance–Inductance Parameter

An ultrasensitive debris sensor based on microfluidic technology is introduced in this article. The microsensor not only achieves the traditional inductance detection, but also proposes a resistance detection method based on coil. The magnetic field distribution in the detection region was obtained by simulation, and the magnetization and eddy current effects of metal particles in harmonic magnetic field were simulated. The frequency characteristics and signal characteristics of inductance and resistance detection were studied through experiments. High-frequency excitation can enhance the eddy current effect inside the metal particles, thereby helping to improve the resistance detection sensitivity, enhance the inductance detection effect of nonferromagnetic metal particles, and reduce the inductance detection effect of ferromagnetic metal particles. The research results showed that inductance parameter has stronger detection ability for ferromagnetic metals, and resistance parameter has stronger detection ability for nonferromagnetic metals. According to the characteristics of inductance and resistance signals, the accurate distinction of metal debris property and size can be realized. And the size measurement error caused by the debris shape can be reduced by the judgment method based on two parameters. This impedance detection based on inductance and resistance parameters provides a new method for high-sensitivity monitoring of metal debris. The debris microsensor can be used as a sensing element of portable oil monitoring devices, and provide technical support for health assessment, state warning, and fault diagnosis of mechanical equipment.