Faults In Mechanical Systems Research Articles

Small sample gearbox fault diagnosis based on improved deep forest in noisy environments

ABSTRACT Deep forest methods have gradually emerged as a well-liked substitute for conventional deep neural networks in diagnosing faults in mechanical systems. However, in practical industrial applications, limited training data and severe noise interference pose significant challenges to these models. Existing deep forest models have limitations in information extraction, making it difficult to handle the complexities of industrial environments. To better meet the practical needs of industrial applications, this paper proposes an improved deep forest model – sgic-Forest, specifically designed for fault diagnosis of small sample gearboxes in noisy environments. First, we developed a stacked multi-grained scanning module, which enhances the diversity of feature extraction by integrating the advantages of multiple base learners, thereby better addressing the complexities of industrial data. Secondly, we introduced an important feature selection module, which effectively filters out irrelevant information, significantly improving the model’s robustness in high-noise environments. Experiments on two gearbox datasets show that the proposed method outperforms the basic deep forest model and mainstream deep learning methods in terms of diagnostic accuracy under small sample and noisy conditions.

WBUN: an interpretable convolutional neural network with wavelet basis unit embedded for fault diagnosis

Convolutional Neural Network (CNN) is extensively applied in mechanical system fault diagnosis. However, the absence of transparent decision mechanisms in CNNs hinders credibility. To address these challenges, this paper proposes an interpretable wavelet basis unit convolutional network (WBUN). This network incorporates meticulously designed wavelet basis unit (WBU) functions into convolutional layer, creating the interpretable wavelet basis unit convolutional (WBUConv) layer. Convolutional kernels with clear physical significance enable the WBUConv layer to extract fault-related features in both time and frequency domains, enhancing diagnostic performance, and interpreting the CNN’s attention frequency along with the convolutional kernel’s training outcomes. In this paper, three WBU functions are designed to construct the corresponding WBUNs, and their effectiveness and interpretability are verified through three sets of mechanical fault diagnosis experiments. Meanwhile, experimental results demonstrate the WBUConv layer’s remarkable advantages in noise robustness, convergence speed, and strong generalization ability.

Optimal Maximum Cyclostationary Blind Deconvolution for Bearing Fault Detection

Maximum cyclostationary blind deconvolution (CYCBD) is a newly presented blind deconvolution method for extracting faults in mechanical systems. The method presents two challenges, which mainly come from needing to set the cyclic frequency in advance and a defining suitable filter length. To address these issues, an optimal maximum CYCBD method is developed. First, the raw signals are processed by the noise subtraction method to remove the inference of inherent impulses and environmental noise. Furthermore, the cyclic frequency is estimated by calculating the autocorrelation of the envelope signals to reveal the fault-induced impulses. Second, to determine the filter length adaptively and automatically, the residual autocorrelation energy (RAE) ratio is employed as the objective function to optimize the filter length by the improved success–failure method. Finally, the proposed method is applied to extract fault features. The effectiveness of the proposed method is validated with data obtained from an open-access bearing dataset and experimental test rigs.

Research on fault diagnosis method of electromechanical transmission system based on one-dimensional convolutional neural network with variable learning rate

As an important part of many mechanical equipment, the mechanical transmission system is very important to carry out efficient and accurate fault monitoring and diagnosis. Compared with traditional fault diagnosis techniques, such as spectrum analysis, deep learning has been widely used in the field of mechanical system fault diagnosis due to its powerful data expression ability, and has achieved certain research results. One-dimensional convolutional neural network is a widely used model for deep learning, so in this paper, the one-dimensional convolutional neural network (1D-CNN) in the deep learning theory and the vibration signal analysis method are integrated and applied to the fault identification of mechanical transmission system to achieve accurate diagnosis and classification of faults. The experiment is mainly to collect the vibration signal data of different fault states such as broken teeth, cracking, shaft unbalance, bearing wear, and excessive friction of the driven wheel of the mechanical transmission system, it was divided into training set and testing set according to an appropriate proportion, and 1D-CNN was built using Python. The deep learning model deeply analyzed the influence of different data sample sizes and different model parameters on the recognition accuracy, and obtained an ideal diagnostic model based on variable learning rate through parameter adjustment and comparative analysis. This experimental results show that the recognition method based on one-dimensional convolutional neural network can be effectively applied to the fault diagnosis of related mechanical transmission, and has a high diagnosis accuracy.

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems

In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance.

Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in both academia and industry. Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency. This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Deep separable convolution extracts features from the raw bearing vibration signals, during which a 3 × 1 convolutional kernel with a one-step size selects effective features by adjusting its weights. The similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller weights. The spatial dropout regularization method focuses on bearing signal fault features, improving the independence between the bearing signal features and enhancing the robustness of the model. A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability improvement. To validate the effectiveness of the proposed method, we collect raw vibration signals from bearings in eight different health states. The experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.

Importance of condition monitoring in mechanical domain

This paper discusses the importance of condition Monitoring in the mechanical domain. Over the last few decades, researchers have been particularly interested in mechanical condition tracking. Automatic machinery is susceptible to malfunction and progressive degradation from its initial state in harsh environments. For this, a suitable parameter that shows real-time internal conditions and mechanical device fault incidence must be selected; as a result, continuous system monitoring is a more critical part of early detection of failure that can trigger system shutdown, reducing system downtime and saving money. Force imbalances, gearing system failures, bearing failures, shaft loading conditions, and incorrect machine selection for a particular job may all cause faults in mechanical systems. System parameters such as efficiency, vibrations, noise, temperature, strain, wear on movables, and lubrication monitoring are needed to detect the fault. Several early sensors are being used in this case to monitor the status of device parameters in real time. Signal processing methods are used to convert the sensors' signal to an electrical signal, which is then processed and analyzed. The device's smooth operation necessitates parameter sensing and monitoring in-process. The use and benefits of sensors and transducers, as well as advanced signal processing, each have their own set of benefits and drawbacks. The basics of various condition monitoring techniques, approaches to condition monitoring, Condition Monitoring Types, and other related topics will be covered in this article. The aim of this paper was to expand condition monitoring to a variety of mechanical domains, including bearing defect detection, rotary machine vibration monitoring, cutting tool monitoring, machine tool monitoring, electricity, vehicles, gear defect detection, and bearing defect detection, to name a few.

Gearbox fault diagnosis based on bearing dynamic force identification

In the area of gearbox fault diagnosis, to obtain sufficient and effective vibration information, traditional studies are usually conducted from the perspective of optimizing the layout of measuring points. This paper focuses on a gearbox fault diagnosis method based on bearing dynamic force identification. Gearbox bearing dynamic forces under the operating condition are modeled as excitation sources, and vibration signals measured at any position on the housing are modeled as receivers. Then mutual coupling effect of different structure transfer paths of housing on the excitation signals can be quantitatively modeled. The bearing dynamic forces are finally constructed with the transfer function matrix and vibration signals by solving the inverse problem. The identified bearing dynamic forces is capable of clearly reflecting the gear fault characteristics, which outperforms the original vibration signals measured on the gearbox housing due to poor signal quality and the effect of structure transfer paths. Numerical simulation and experimental studies show the effectiveness of the estimated bearing dynamic force signals for gear fault diagnosis. The proposed method is demonstrated to be insensitive to the location of measuring points, and shows a good potential in complicated mechanical system fault diagnosis.

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.

Faults detection and classification in a centrifugal pump from vibration data using markov parameters

One of the strategies to detect and classify faults in mechanical systems is to use a time domain family of techniques known as output-only methods. Those methods are based on the analysis of sample covariance matrices, which are estimated from vibration data extracted from mechanical systems under unmeasured natural excitation. Using the stochastic realization theory, it is possible to derive Markov parameters from sample covariance matrices. Those parameters contain only the significant spectral components from data. In this paper, a novel output-only method based on the Markov parameters is proposed to diagnose faults. The idea is to use the Markov parameters estimated from vibration data as features in classification algorithms based on convex optimization. The method was applied to diagnose incipient cavitation failures in a water supply network centrifugal pump. A low-cost triaxial vibration sensor developed by one of the authors was used to register the vibration data. The proposed method was compared to the analysis based on sample covariance matrices demonstrating the advantages related to the use of the Markov parameters.

Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification

Fault identification is a vital task to ensure the integrity and reliability of rotating machinery. The vibration signals produced by the defective system components typically bear a significant amount of noise, including non-linear and non-stationary characteristics induced by the intricate operational environment. Advanced signal processing technologies still have difficulty in detecting faults in mechanical systems. This paper presents a wise local response convolution neural network-based Naive Bayes algorithm (WCNN-NB) to identify multiple faults in rotating machines. In WCNN-NB, the WCNN structure is first used to characterize the features of the gray-scale images transformed from the original vibration signals. The nonlinearity parameter value of WCNN is explored in order to enhance learning efficiency. The NB algorithm is then used as a robust steady-state method to identify the learned features. Experimental data regarding helical gears and bearing test rigs are used to validate the feasibility of the WCNN-NB model. The superiority of the classification results is verified by comparing the current model with the WCNN-support vector machine, WCNN-random forest, standard CNN, the support vector machine (SVM) and the neural backpropagation network (BP) models. The results demonstrate that the classification accuracy is 99.68%, 92.5% and 97.5% for three data sets with tolerable misclassification rates under all operational conditions.

Mechanical Fault Diagnosis Methods Based on Convolutional Neural Network: a Review

Deep learning is good at abstract features from massive data and has good generalization ability, which has attracted more and more researchers’ attention. The Convolutional Neural Network (CNN) is a classic structure of deep learning and which is being widely and successfully used in the fields of computer vision, target detection, natural language processing, and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis, this paper introduces the structure of CNN and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type, network structure design, and migration learning. The problems of deep feature extraction and visualization are also discussed, and finally, the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN prospect.

A Novel Method for Predicting Fault Labels of Roller Bearing by Generalized Laplacian Matrix

Because mechanical failures are accompanied by contingency and randomness, fault data is often difficult to obtain, and fault labels are also difficult to assign. The lack of data and fault labels have become important issues that restrict the development of fault diagnosis. The paper proposed a generalized Laplacian label prediction (GLLP) algorithm, which mainly uses the generalized Laplacian matrix and calculated a new locally smooth term. Therefore, data points with ambiguous and unclear labels will be assigned a small label value, while samples with more certain labels can get a more confident label value. The effectiveness of the method is verified on the public dataset and the real test rig dataset, and it is expected that this method can be extended to more complex mechanical system fault diagnosis.

Noise Analysis of Air Disc Brake Systems Using Wavelet Synchro Squeezed Transform

Click here for manuscript sample templateRecently, signal processing methods are shown to be successful while diagnosing faults in mechanical systems, using noise or vibration data. In this study, two different faulty air disc brakes; noisy and less noisy ones are investigated using Wavelet Synchrosqueezed Transform on audio recordings. The difference between two types are shown in scalogram and also verified by a quantitative measure of entropy. The audio recording has been carried out by using two identical microphones sited on the brakes via data acquisition unit at a sampling rate of 20 kHz, 16-bit resolution and these data are analyzed in MATLAB software. The average of the entropy values of faulty and non-faulty brakes were found to be 0.98 and 0.65, respectively. Therefore, it has been concluded that, the entropy could be used as a distinguishing tool to discriminate the faults.

Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems

The raw vibration signal of a faulty mechanical component carries a large amount of information reflecting its health condition, and the faulty information is typically carried by the high-frequency term in the vibration signal. However, the high-frequency term can easily by overwhelmed by the interference from the low-frequency term and noise. Considering the elimination of interference of the low-frequency term a novel preprocessing technique is presented, one-level kernel regression residual decomposition (KRRD), which can be used to extract the high-frequency term from the raw vibration signal to track the fault information. Combined with the synchroextracting transform (SET) technique, a one-level KRRD-based SET method is proposed. First, the high-frequency term in the raw vibration signal, which contains the faulty information, is extracted using one-level KRRD. Then, the high-frequency term is purified using SET, and the signal-to-noise ratio (SNR) is increased. Finally, a Hilbert envelope analysis is applied to the purified signal to demodulate the faulty feature frequency. To validate the performance and necessity of the proposed method, numerical simulations and experimental investigations are conducted. By introducing two commonly used methods, i.e., empirical mode decomposition (EMD) and variational mode decomposition (VMD), four comparisons (KRRD & EMD, KRRD & VMD, EMD, VMD) are conducted, and the superiority of the proposed method is verified. The analysis results show the effectiveness of the one-level KRRD-based SET method for the detection of mechanical component faults

Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring

Induction motors have been responsible for running mechanical systems in the industry for many decades. Their diagnosis still remains a hot quest for the researchers using various techniques. In this study, motor current signature analysis (MCSA) technique has been used to detect the faulty bearing installed in load machine (coupled to an induction motor). It has been seen that faulty bearings installed in load machines do not directly alter airgap eccentricity of an induction motor. In fact, these bearing faults affect the resultant torque of an induction motor. As modulating fault components show very low amplitude, these are usually masked by noise. This paper is devoted towards extracting features of faulty components efficiently from stator current using continuous wavelet transform. This methodology is assessed for detecting outer race faults in bearings installed in load machines using MCSA.

Stochastic Resonance algorithms to enhance damage detection in bearing faults

Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.

Signal Measurement Error Analysis of the Bayesian Network of Mechanical Fault Detection

Based on measurement error of observation nodes is commom in mechanical system fault detection, but the traditional denoising method has many shortcomings. This paper introduce the Gibbs sampling method, which can be used to denoise and eliminate measurement error for node discreted information. We discuss it, and expect some promotion in practical application.

Research on Internet-Based Reconfigurable Distance Mechanical System Fault Diagnosis

In order to meet requirements of increasingly high-speed, large and intelligent mechanical equipments on fault diagnosis, the Internet-based reconfigurable mechanical system fault diagnosis program was presented. The overall structure and networking schema of distance mechanical fault diagnosis system were analyzed, and the distance fault diagnosis network model based on J2EE framework was also described. The structural model and reconfigurable manner of the reconfigurable distance diagnosis system was provided, which used CORBA component technology to achieve reconfiguration. The detail steps of system that take some type of diesel engine as diagnosis object was described, and the intelligent diagnosing methods were also researched. The Internet-based fault diagnosis technology effectively improves the efficiency and accuracy of diagnostic systems.