Fault Diagnosis Of Mechanical Equipment Research Articles

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Bearing fault diagnosis based on enhanced Canberra distance feature in SDP image

Abstract Feature enhancement is important in mechanical equipment fault diagnosis. A limited set of characteristic parameters is insufficient for diagnosing bearing signals with multiple fault types. The presence of noise increases the difficulty of extracting fault features from images. To address the challenge of diagnosing rolling bearing faults in complex environments, this study presents an enhanced weighted image fusion framework aimed at enhancing fault features within the images, which enables accurate diagnosis of bearing faults using a limited number of features. The proposed method encompasses four distinct stages. In the first stage, a symmetrized dot pattern method is employed to transform one-dimensional time-series data into two-dimensional images, visualizing the signal in a 2D format. In the second stage, image binarization and an improved weighted fusion method are utilized to simplify subsequent processing and enhance the image features. The third stage involves extracting the image’s contrast and maximum singular value to improve the Canberra distance calculation. Finally, the enhanced Canberra distance is used for classifying bearing faults. Performance testing of the image feature enhancement is conducted on various datasets containing rolling bearings. Comparative experiments with alternative enhancement methods demonstrate the superiority of the proposed improved weighted image fusion framework. Comparative experiments with the original Canberra distance validate the effectiveness of the enhanced Canberra distance. Additionally, experiments conducted in noisy environments confirm the robustness of the proposed approach. Furthermore, the image feature enhancement method is applied to other bearing datasets, and the experimental results demonstrate its effectiveness in enhancing fault feature representation and achieving accurate diagnosis of rolling bearings.

A bearing fault data augmentation method based on hybrid-diversity loss diffusion model and parameter transfer

The fault diagnosis of mechanical equipment can prevent potential mechanical failures, avoid property damage and personal injury, and ensure the stable and safe operation of mechanical equipment. Data driven is an important aspect of intelligent fault diagnosis. When data is scarce, it can seriously affect the accuracy of fault diagnosis and make it difficult to ensure the smooth and safe operation of machinery. Faced with this challenge, a classifier-free guidance diffusion model combining hybrid loss and diversity loss (CFGDMHD) is proposed for data augmentation of fault samples. This new data augmentation method generates samples with the same data distribution as real samples from random noise through diffusion process. CFGDMHD can generate multi-class samples simultaneously without the need for additional classifier guidance in the joint training of unconditional diffusion models and conditional diffusion models. This work proposes diversity loss to improve the diversity of generated samples. We conducted experiments using a bearing dataset. The results indicate that the sample quality and diversity generated by this method are excellent, which can help improve the accuracy of fault diagnosis and ensure the safe operation of mechanical systems.

Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment.

Fault diagnosis is vital for improving the reliability and safety of mechanical equipment. Existing fault diagnosis methods require a large number of samples for model training. However, in real-world environments, mechanical equipment usually operates under healthy conditions during most of its service life, resulting in a scarcity of fault samples. To solve this problem, a novel multilayer fusion correntropy representation method combined with a support vector machine is proposed for the fault diagnosis of mechanical equipment. First, the monitoring signal is expanded into multilayer signal components using wavelet packet decomposition. Then, the correlation between the signal components of each layer is expressed by correntropy, and the corresponding correntropy matrix is constructed. After performing the matrix logarithm operator, all correntropy matrices composed of correntropy values are fused into a vector, which is viewed as a feature of the signal. Finally, a support vector machine is established using small samples to realize fault classification. The effectiveness of the proposed method is validated on four public datasets. The results indicate that compared with other methods, the proposed method has advantages in terms of diagnosis accuracy and noise immunity ability.

Research on Fault Prediction and Diagnosis of Mechanical and Electrical Equipment in Construction Machinery based on Big Data Technology

This article aims to explore how to use big data technology for fault prediction and diagnosis of mechanical and electrical equipment in construction machinery. Through steps such as data collection and storage, data feature extraction and selection, and data analysis and processing, a fault prediction and diagnosis model is constructed to achieve real-time monitoring and early warning. The accuracy and efficiency of fault prediction are improved through decision feedback and algorithm optimization.

Research on rolling bearing fault diagnosis technology based on singular value decomposition

To solve the difficulty of selecting the number of effective singular values in Singular Value Decomposition denoising, a new method to determine the number of effective singular values is proposed. The proposed method to determine the number of effective singular values is based on the non-zero singular value distribution law of the Hankel matrix constructed by the signal. Specifically, the number of effective singular values in the Hankel matrix is twice the number of frequencies contained in the signal, and the difference between the effective singular values of the noisy signal and the non-zero singular values of the pure signal is very small. The proposed method for determining the number of effective singular values is to perform differential processing on the singular values of the signal and normalize the difference obtained. An empirical parameter T is provided, and the number of effective singular values is determined by comparing them with the normalized results. The proposed method is applied to the simulated and measured rolling bearing signals, and the results are compared with the wavelet threshold denoising method. The results show that the proposed method for determining the number of singular values can effectively filter out the noise frequency contained in the signal while maintaining the characteristic frequency of the signal and achieving the purpose of mechanical equipment fault diagnosis.

Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing

The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

Intelligent Fault Diagnosis Method Based on Cross-Device Secondary Transfer Learning of Efficient Gated Recurrent Unit Network.

In response to the issues of low model recognition accuracy and weak generalization in mechanical equipment fault diagnosis due to scarce data, this paper proposes an innovative solution, a cross-device secondary transfer-learning method based on EGRUN (efficient gated recurrent unit network). This method utilizes continuous wavelet transform (CWT) to transform source domain data into images. The EGRUN model is initially trained, and shallow layer weights are frozen. Subsequently, random overlapping sampling is applied to the target domain data to enhance data and perform secondary transfer learning. The experimental results demonstrate that this method not only significantly improves the model's ability to learn fault features but also enhances its classification accuracy and generalization performance. Compared to current state-of-the-art algorithms, the model proposed in this study shows faster convergence speed, higher diagnostic accuracy, and superior robustness and generalization, providing an effective approach to address the challenges arising from scarce data and varying operating conditions in practical engineering scenarios.

Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey

Abstract Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

The research on triple-coil inductive debris sensor with electromagnetic shielding

A high sensitivity inductive debris sensor is of great significance for oil condition monitoring and the fault diagnosis of mechanical equipment. However, inductive sensors are susceptible to interference from external electromagnetic fields. In this study, an inductive debris sensor with a shielding layer is studied, which can effectively reduce the influence of external magnetic fields on the sensor. Theoretical and experimental results verify the effectiveness of the shielding layer, and also show that the shielding layer impacts the magnetic field of the sensor, resulting in a decrease in the amplitude of the output signal. Detailed analysis was conducted to investigate the effect of structural parameters on the performance of inductive sensors. These parameters include the distance between the shielding layer and the coils, the thickness of the shielding layer, and the shielding layer material. Based on the theoretical and experimental results, reference suggestions are provided for the design of the shielding layer. This study ensures the performance and reliability of inductive sensors in practical applications.

Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions

The speed variation poses great hardships to the intelligent fault diagnosis of mechanical equipment. Existing solutions rarely consider the interpretable representation of fault information, and still suffer from the “black box” issue that weakens their own practicability and credibility. Motivated by these issues, this study mathematically derives the modal response model of fault-impulsive signals of rotating machinery under variable speed conditions. On this foundation, an interpretable architecture collaborating signal processing with deep learning—prior-knowledge-guided mode filtering network (PKG-MFNet) is proposed, which consists of three sub-structures: mode filtering layer, prior knowledge pooling layer and classifier. In the mode filtering layer, multiple FIR filtering kernels are first constructed by an explainable speed fusion strategy. Each filtering kernel has a center frequency and a bandwidth coefficient fitted from the speed, which is used to extract fault-sensitive modes under varying speed conditions as the explainable feature representations of fault information. Subsequently, the extracted modes are pooled into 12 modal prior indicators (MPIs) that represent health status information in the prior knowledge pooling layer. Finally, the classifier employs two fully-connected layers to make the final decision. Different from the conventional interpretable network for speed information characterization, the speed information is innovatively fused into a novel mechanism framework for sensitive information mining with rigorous theoretical support. Extensive experiments are conducted to verify the superiority of the proposed method over six state-of-the-art methods. Particularly, the visualization analysis not only demonstrates the mode filtering capacity of FIR filtering kernels under variable speed conditions, but also interprets the guiding significance of MPIs for the final decision.

Railway wagon bearing fault diagnosis method based on improved sparrow search algorithm optimizing variational mode decomposition and multi-level convolutional neural network.

Ensuring the safe operation of trains hinges on precise bearing condition monitoring, given the pivotal role bearings play in railway wagons. The status and maintenance of wagon bearings are of paramount concern, necessitating a shift from traditional maintenance approaches reliant on schedules and experience, which often lack real-time precision and efficiency. To address this challenge, our research focuses on enhancing the sparrow search algorithm by incorporating logistic chaos mapping and the levy flight strategy. This enhanced algorithm optimizes variational mode decomposition parameters, utilizing intrinsic mode components' average dispersion entropy as the fitness function. This optimization is integrated with a multi-level convolutional neural network for bearing fault diagnosis. Our findings demonstrate the improved algorithm's enhanced spatial search capabilities and reduced modal aliasing in the frequency components. Experimental validation on public datasets and the group's experimental platform for railway wagons shows that multi-level convolutional neural networks have higher diagnostic accuracy and faster convergence speeds than traditional models such as LeNet-5, AlexNet, and convolutional neural network. Our research introduces a highly accurate and widely applicable methodology for mechanical equipment fault diagnosis, aligning with the requirements of the "smart" era.

Amplitude-based multiscale reverse dispersion entropy: a novel approach to bearing fault diagnosis

The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the characteristics from a variety of complex nonlinear signals, including bearing signals, due to its excellent performance to quantify the uncertainties of complex nonlinear systems. However, limited by the classification number and coarse-graining process, the periodic impulses generated by the defect point cannot be effectively detected by MFDE, restraining the characterization abilities of entropy features and resulting in undesirable diagnosis results for bearing faults. To overcome the disadvantages of MFDE, an amplitude-based multiscale dispersion entropy (AMDE) is proposed in this paper. The AMDE utilizes the phase scale factor to calculate multiple groups of amplitude difference series that contain different amplitude information. As such, the amplitude compression caused by the large-scale factor in traditional coarse-graining process is avoided, and the calculated entropy features not only characterize the irregularity of the whole signal but also reflect the changes of the impulse components. Afterwards, the perception range and the sensibility of AMDE are expanded and enhanced for amplitude variation, and the coarse-graining process and Gaussian reference are used to obtain multi-dimensional reversed entropy features. Combining those steps, the amplitude-based multiscale reverse dispersion entropy (AMRDE) algorithm is proposed. Finally, the capability of the proposed algorithm to track the amplitude variation and fluctuation is successfully demonstrated by analyzing noisy signals and amplitude-modulated signal. Meanwhile, the features extracted from bearing signals demonstrated that it is effective to use AMRDE to represent the health conditions of rolling bearing. Therefore, the entropy metric calculated by AMRDE can be the useful indicator in the fields of mechanical equipment fault diagnosis, structural health monitoring, and so on.

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★ ★This work is supported by National Natural Science Foundation of China [No. 61603149]; Shandong Province Natural Science Foundation [No. ZR2023MF102

In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.

Self-matching extraction fractional wavelet transform for mechanical equipment fault diagnosis

Time-frequency analysis (TFA) of vibration signals is an important way for fault diagnosis and predictive maintenance of mechanical equipment. However, the existing TFA methods, such as wavelet transform (WT) and synchrosqueezing transform, are incapable to describe local details of strongly time-varying signals, bringing such problems as time-frequency energy dispersion. Theoretically, it is more beneficial for time-varying band-limited signals to make multi-resolution analysis in the time-fraction frequency domain because such action can increase the selection chances of mother wavelets in any direction on the time-frequency plane by changing the angle . In order to improve the ability of analyzing strongly time-varying signals and the readability of the time-frequency plane, this paper has proposed a novel TFA method called self-matching extraction fractional WT (SMEFRWT). Firstly, within the theoretical framework of fractional WT (FRWT), a minimum entropy-based method is proposed to choose an optimal angle in the fractional domain. Such action can obtain the best aggregation angle of time-frequency energy and facilitate the separation of multi-component signals. Then, self-matching extraction operator, one instantaneous-frequency estimated operator that match the time-frequency structure of amplitude modulation signals, is constructed. The operator allows for the distribution of time-frequency energy of fast-changing signals in both frequency and time directions, making it possible to match the time-varying vibration signals from mechanical equipment more accurately. Finally, SMEFRWT is adopted to make TFA and extract fault features of the faulty experiment benches on gear and bearings, with its feasibility validated by experimental results.

A study on fiber Bragg grating (FBG) strain sensor based on symmetrical lever structure.

Thin-walled structure deformation detection technology is one of the key technologies for structural health monitoring and fault diagnosis of high-end mechanical equipment. Aiming at the problem that the existing Fiber Bragg Grating (FBG) strain sensor is difficult to effectively measure the deformation of thin-walled structures, an FBG strain sensor based on a symmetrical lever structure is proposed. The sensitivity of the sensor is analyzed theoretically, and the sensor is simulated and analyzed by the SOLIDWORKS and Abaqus software, and then, the structural parameters are optimized. According to the simulation results, the sensor is developed and a strain testing system is set up to test the performance of the sensor. The results indicate that the sensor sensitivity is ∼6.6 pm/με, which is about 5.5 times that of bare FBG. Its strain measurement sensitivity and stability are much higher than those of bare FBG, thus meeting the strain detection requirements of thin-walled structural parts during deformation. Moreover, the linearity is more than 99%, which enables the accurate measurement of tiny strains caused by the deformation and reconstruction of the thin-walled structure by the strain sensor. The results of this study provide a reference for the development of like sensors and a further improvement in the sensitivity of the optic-fiber strain sensor.

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

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

Prediction of mechanical equipment fault diagnosis based on IPSO-GRU deep learning algorithm

Abstract Exploring effective logistics machinery and equipment fault diagnosis and prediction technology to achieve efficient and stable operation of logistics machinery and equipment. In this paper, starting from the logistics machinery and equipment fault diagnosis technology, we optimize the hyperparameters of the recurrent gate unit neural network by using the improved second-order oscillatory particle swarm algorithm and then construct the IPSO-GRU logistics machinery and equipment fault prediction model. The IPSO-GRU model is used to test the prediction effect of the hydraulic lift table and logistics hoist by using the historical data of the hydraulic lift table as training data. The prediction accuracy of the IPSO-GRU model was improved by 6% compared with BP neural network. From the prediction results of the logistic hoist, only 6 out of 250 data samples failed to achieve accurate prediction. This shows that the IPSO-GRU model can effectively achieve the prediction of logistics machinery and equipment fault diagnosis and also provides a proven method for predictive maintenance of logistics equipment.

C-ECAFormer: A new lightweight fault diagnosis framework towards heavy noise and small samples

In engineering practice, small-sample fault diagnosis of mechanical equipment towards heavy noise interference poses great challenges for the existing Transformer based intelligent models. To address these challenges, this paper proposes a new lightweight model called C-ECAFormer. Firstly, inverted residual block is used to establish signal correlations and inductive bias capability and to extract richer local feature information by varying the input channel dimensions. Secondly, ECAFormer module is designed to enhance the relationship awareness between different channels in the input signal features, thereby improving the model's attention to important channels. Finally, collaborative self-attention block is developed to facilitate spatial interaction between window local and grid global in vibration signals, reducing the number of parameters and computational complexity of the model. The results of two experiments demonstrate that the proposed approach accommodates advantages of lightweight and robustness in small-sample fault diagnosis tasks, compared to the existing mainstream Transformer and CNN fault diagnosis frameworks.

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.