Gear Fault Diagnosis Research Articles

A feature extraction method based on moving multi-scale reconstruction and interactive energy entropy for gear fault diagnosis

In order to accurately extract the sensitive features representing the type and severity of gear faults through the vibration signal, a gear fault diagnosis method using moving multi-scale reconstruction-based interactive energy entropy (MMS-IEE) is proposed. The gear vibration signal is reconstructed using a multi-scale mean at different scales and adjacent data points are used to form a sliding window, which makes the information extraction from the vibration signals sufficient. The energy distributions of the original signal and the reconstructed signal under different scale channels are calculated. Compared with the traditional energy entropy (EE) method, the feature vector obtained by the interactive superposition method can more accurately represent the energy mutation of the time-series caused by the fault. Experimental results show that the proposed MMS-IEE method has a strong fault feature extraction ability and high gear fault diagnostic accuracy under different speeds and working conditions.

Gear Fault Diagnosis Under Variable Load Conditions Based on Acoustic Signals

The health condition of gears has been a topic of study in the past few decades due to the importance of gears for the transmission system. In recent years, some studies have used acoustic signals for gear diagnosis, which can overcome the limitation of vibration signals through noncontact measurement by air-couple. Although many acoustic-based diagnosis (ABD) methods have achieved good diagnosis performance of gear in stable working conditions, these methods suffer from effectiveness loss as the change of working load condition in the actual industry causes the domain shift problem. To overcome the above shortcoming, a domain-adversarial neural network (DANN) with a temporal attention mechanism (TAM) and a high dropout mechanism (HDM) is proposed in this article, which uses the acoustic signal of gears as the input of the model to detect gear health condition. First, the confrontation between the feature extractor and the discriminator in DANN is used to extract domain-invariant features for solving the domain shift problem. Then TAM is introduced into the feature extractor in DANN to refine domain invariant features for further enhancing domain adaptation ability to improve the diagnostic performance. Finally, HDM is utilized to erase the neurons of the input of the classifier with a random high probability to enhance the generalization ability of the model for further improving the classification performance. The experimental results show that the proposed method is effective to solve the domain shift problem of acoustic signals under variable load conditions.

A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes

Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods.

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

Maximum correlation Pearson correlation coefficient deconvolution and its application in fault diagnosis of rolling bearings

Blind deconvolution (BD) has been proved to be an effective tool for recovering periodic impulses from strong background noise signals is widely used for fault diagnosis of bearings and gears. However, the existing BD focus more on improving the quantity and amplitude-frequency characteristics of interested periodic impulses without considering their phase-frequency characteristics and impulse waveform characteristics (attenuation coefficient and oscillation frequency), which results in severe distortion of the reconstructed signal. In this scenario, a new BD method, named maximum correlation Pearson correlation coefficient deconvolution (MCPCCD), is presented in this paper. Firstly, a new objective function is constructed which include two terms: correlation Pearson correlation coefficient (CPC) and signal fidelity term (SFT). CPC is a new norm with strong sensitivity to interested impulse signals, while SFT is a dimensionless error measure similar to RMSE. Then, the optimal FIR filter is obtained by maximizing the objective function of the reconstructed signal. Meanwhile, a new preprocessing step is designed to reduce the requirement of MCPCCD for periodic prior. Finally, the application on simulated and two experimental signals indicates that MCPCCD significantly outperforms the traditional BD method in recovering the periodic impulses.

A composite learning approach for multiple fault diagnosis in gears

A major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.

Fault Detection and Diagnosis of Gear Train Based on Motor Current Signature Analysis

Gear fault diagnosis generally uses oscillation signals to extract fault features, but the oscillation identify technique is inconvenient to set up sensors and is easily affected by the surrounding and noise. The motor itself has the property of a sensor, and signals such as motor current is able to reflect the change of the load torque. This paper proposes a gear fault detection approach, which applies the Motor Current Signature Analysis (MCSA) to exploit spectral characteristics to recognize malfunction in servo motor drive gears. First, we calculate the nominal revolutions per minute (rpm) to explore frequencies of interest. Second, frequency bands where faulty signals would be appear are constructed. Next, we extract power spectral density (PSD) feature data. Finally, the paper computes spectral metrics for the frequency band of interest. By using two spectral metrics, gear health and failure data are clearly grouped in different areas of the scatter chart. The experimental results give evidence of the effectiveness of analyzing current characteristics of servo motors to classify fault and health data.

Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning

The planetary gearbox is a key transmission apparatus used to change speed and torque. The planetary gear is one of the most failure-prone components in a planetary gearbox. Due to the complexity of working environments, collected vibration signals contain a lot of noise and interference; fault characteristic frequencies are usually submerged or even lost. Thus, feature extraction from the vibration signal is beneficial to subsequent fault diagnosis. As a fault identification approach that has been increasingly popular in the field of fault diagnosis, deep learning requires a large number of samples to train the model. Insufficient samples lead to low diagnostic accuracy for deep learning models. This paper proposes a novel fault diagnosis approach for planetary gears based on intrinsic feature extraction and deep transfer learning. The original vibration signals are decomposed into a series of band-limited intrinsic mode functions (BLIMFs) by variational mode decomposition. BLIMF with the most apparent fault characteristics is selected to generate two-dimensional time-frequency maps by continuous wavelet transform. The preprocessed time-frequency maps are adopted as the input of the pretrained VGG16 model. The bottom layers are frozen, and the top layers are fine-tuned to achieve fault diagnosis for planetary gears. Applications to planetary gear datasets verify the superiority of the proposed method.

Mesh characteristic analysis and dynamic simulation of spur gear pair considering corner contact and tooth broken fault

Theoretically, the contact of a spur gear pair occurs on the line of action (LOA), but the corner contact may happen due to the tooth deformation, especially when there is a tooth broken fault. In this paper, based on the tooth contact analysis, the initial gaps considering tooth broken fault are accurately calculated. Meanwhile, the initial gaps are brought into the loaded tooth contact analysis program and yield the time-varying mesh stiffness (TVMS) and non-load transmission error (NLTE). By comparing with the finite element (FE) method, the meshing state of the gear pair considering various tooth broken heights is presented. The result shows that it is inaccurate to regard the mesh stiffness value as zero when the missing tooth profile should have contact since serious corner contact phenomenon will occur in this state. Moreover, by comparing the simulated dynamic response with the experimental results, this paper finds that the simulation result considering the corner contact effect is closer to the experimental result. This study can provide a more accurate model for the diagnosis of gear tooth broken faults.

Gear Fault Diagnosis Based on Variational Modal Decomposition and Wide+Narrow Visual Field Neural Networks

In modern industrial production, rotating machinery plays an important role. The gears in this machinery adjust the speed and transmission of torque. Therefore, when the gear fails, it is very important to be able to diagnose the fault quickly and accurately. Gear vibration signals are often used in gear fault diagnosis, but the fault signal is often overwhelmed by noises. To enable the scientific and efficient detection of faults, this study proposes a gear fault diagnosis method based on variational modal decomposition (VMD) and wide+narrow visual field neural networks (WNVNNs), namely VMD-WNVNN. VMD-WNVNN consists of two stages. In the feature extraction stage, VMD and Pearson correlation coefficients are used to decompose and reconstruct the original data to obtain the features of these data in the frequency domain. In the classification stage, WNVNN is used to classify the data based on features. The final results of the gear fault diagnosis experiments show that this method not only has higher classification accuracy but also has higher classification stability than other recently proposed methods. Note to Practitioners—The gearbox is composed of many mechanical parts, such as gears, shafts, and bearings. Therefore, the vibration signal collected by the vibration sensor on the gearbox housing will contain the vibration signal of each part and the noise caused by processing errors. Therefore, if some methods can be used to efficiently extract the characteristic signals required for diagnosis in the data processing stage, the efficiency of fault diagnosis will be greatly improved. This article takes the health of gears as the research object and proposes a method that combines adaptive signal decomposition and deep learning technology. Experimental results show that this method has higher classification accuracy and classification stability than other methods proposed recently.

Enhanced Sparse Low-Rank Representation via Nonconvex Regularization for Rotating Machinery Early Fault Feature Extraction

The weak fault feature extraction is key to early fault diagnosis of rotary machinery. However, the existing sparse low-rank fault feature extraction methods have the deficiency of underestimation and low peak signal-to-noise ratio. To solve these problems, this article presents an enhanced sparse low-rank (ESL) representation approach for weak fault feature extraction. Considering the periodic self-similarity and shift invariance of fault feature, a weighted dual approximation regularization is proposed for noise and fault irrelevant harmonic suppression, which provides a cornerstone for rotating machinery weak fault feature extraction. To be specific, the truncated nuclear norm (TNN) and weighted generalized minimax-concave (WGMC) penalty are leveraged to form the weighted dual approximation regularization so that it can inherit their superior properties. The TNN can capture the periodic self-similarity and shift-invariance structure of fault impulses while restraining the noise; the WGMC penalty can enhance the sparsity, restrain the fault irrelevant harmonics, and overcome the deficiency of underestimating the large amplitude components. Therefore, the proposed model can effectively extract the weak fault feature. The proposed approach is applied to bearing and planet gear fault diagnosis to evaluate its effectiveness. Comparison results show the significant improvements of the proposed ESL method, indicating that it has great potentials in fault diagnosis of rotating machinery.

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations

Fault detection and diagnosis of gear systems using vibration measurements play an important role in ensuring their functional reliability and safety. Computational intelligence, leveraging upon classification through various surrogate models, has recently demonstrated certain level of success. Major challenge however remains. The establishment of surrogate models generally requires large size of training data with specific labels corresponding to explicitly known gear fault conditions, which may not be available in practical applications. Both the size of available data and the respective labels may be quite limited due to the high cost, which hinders the diagnosis of unseen/unexpected faults with desired reliability. In this research we synthesize a deep convolutional generative adversarial network (DCGAN) to tackle this challenge. This new approach follows the semi-supervised learning concept, the performance of which is significantly enhanced by introducing additionally the inexpensive unlabeled data. The balanced adversarial effect between the discriminator and generator in DCGAN is realized by appropriately designing their architectures, which as a result can enable the high accuracy of diagnosis with scarce labeled data. More importantly, by taking full advantage of the rich fault signatures in the unlabeled data that point to the diverse unseen faults, the intrinsic correlation of underlying physics between the unseen and known faults can be implicitly elucidated via unique semi-supervised learning strategy featured in DCGAN. Therefore, the extended capability in diagnosing the unseen faults that are beyond the known faults in training dataset can be realized, which bears practical significance. Systematic case studies using experimental data acquired from a lab-scale gear system are carried out to validate the new diagnosis framework.

Numerical simulation of gears for fault detection using artificial intelligence models

Artificial intelligence (AI) models are widely used in every field of life to classify and predict the abnormal conditions. The key to activating AI models is to obtain enough training samples. The lumped parameter model is the one of the best choices to provide sufficient samples. In this study, the lumped parameter model is used in the numerical simulation of gears to obtain enough training samples to detect their running state. The aim is to replace the traditional sample generation method with the modified lumped parameter numerical method to realize the fault diagnosis of gears. Firstly, the lumped parameter model of gears is constructed and further updated. Secondly, failure types are inserted into the updated lumped parameter model and generate samples. The set composed of measured signals and simulated signals is equally divided, and the corresponding time-domain signal indexes are calculated to form a sample matrix for AI training and classification. Finally, CNN, RNN and LSTM are selected as the representatives of AI model, and then the trained models are used to classify the unknown measurement fault samples. The classification model trained by the simulation fault samples has almost 100% accuracy for the current gear fault classification, which shows that it is a feasible way to generate sample data for artificial intelligence fault classification by using the lumped parameter models.

Ramanujan Fourier Mode Decomposition and Its Application in Gear Fault Diagnosis

As an important part of rotating machinery, gear is easy to appear some unexpected fault states, and its fault diagnosis is very important. Fourier decomposition method (FDM) is a common method for gear fault diagnosis, but the noise robustness, period recognition, and extraction capabilities of FDM are unsatisfactory. Based on this, in this article, Ramanujan Fourier mode decomposition (RFMD) method is proposed. The RFMD not only has a complete mathematical theory foundation but also has an excellent ability to identify and extract periodic components. Emulational and experimental results of planetary gearbox show that the RFMD method has good noise robustness and can accurately extract gear fault characteristic information. Thus, it is an effective gear fault diagnosis method.

Rinser Machine and the Dynamics-based Failure Analysis for Spur Gear Fault Detection: A Forensic Review

Gear fault detection and its severity is essential for analyzing the availability, maintainability and reliability of rinser machines. The occurrence of multiple failures of gears and their analysis is becoming more challenging, thereby causing the increased unavailability of rinser machines. This study reviewed the rinser machine and its various advantages and disadvantages, including the problems associated with the machine. More so, various types of failures associated with the spur gears were studied, and it was established that the detection and the diagnosis of gear faults would inform a maintenance planner on scheduled maintenance. Thus, the absence of failure detection will cause uncontrolled machine breakdown and, consequently, result in unsafe operation as well as a loss of productivity. The study concludes and recommends grey cast iron as alternative material that could reduce chemical failure and failure resulting from fatigue.

Completely adaptive projection multivariate local characteristic-scale decomposition and its application to gear fault diagnosis

Multichannel signal can be quickly and effectively decomposed using multivariate local characteristic-scale decomposition (MLCD). However, multi-channel signals acquired by sensors often have power imbalance characteristic, and MLCD uses uniform projection to estimate the baseline, which leads to inaccuracy of MLCD in decomposing such signals. Additionally, uniform projection introduces some projection vectors that can't precisely describe the multichannel signal, based on which, completely adaptive projection (CAP) is proposed in this paper. Completely adaptive projection multivariate local characteristic-scale decomposition (CAPMLCD) is developed on the basis of CAP. The simulation and experimental results show that CAPMLCD and MLCD perform better in terms of suppressing mode mixing, decomposition efficiency, and decomposition accuracy than multivariate empirical mode decomposition (MEMD) and adaptive projection intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD). By comparing with MEMD, APIT-MEMD and MLCD, CAPMLCD has better decomposition accuracy and lower dependence on the number of projections.

Gear Fault Diagnosis and Life Prediction of Petroleum Drilling Equipment Based on SOM Neural Network.

In order to solve the problem that variable working conditions and fault types cannot be diagnosed in gear fault diagnosis of petroleum drilling equipment, four kinds of faults, namely, gear broken tooth, gear crack, gear pitting, and gear wear, are studied in this paper. Based on the SOM neural network algorithm, an intelligent diagnosis model of gear fault is proposed, and the PCA method is used to reduce data dimension and fuse features. The state index of life prediction is determined, and the remaining service life prediction of gear transmission system is predicted based on exponential degradation model. The results show that the accuracy of the SOM model for fault diagnosis is high, and the bearing in gearbox can be replaced or repaired in advance according to the residual life curve, so as to achieve the purpose of predictive maintenance.

Fault feature analysis and detection of progressive localized gear tooth pitting and spalling

Fault feature analysis of gear tooth spalling plays a vital role in gear fault diagnosis. Understanding how fault features evolve as a fault progresses is key to fault severity assessment. Due to the complicated nature of gear meshing, fault features and their development as the fault severity progresses remain mostly unknown. The assessment of fault severity is generally based on the hypothesis that ‘the more severe the fault, the stronger the fault symptom’, an assumption that has not been experimentally validated. This paper provides a comprehensive, experimental analysis of the evolution of fault vibration features of a gear transmission with progressive localized gear tooth spalling. The effects of rotational speed on the vibration features of the gear transmission are analysed. Changes in fault features (e.g. periodic impulses and sideband phenomena) under different fault severity levels and speed conditions are compared. Results indicate that the number, amplitude and distribution of sidebands increase nonlinearly as the fault progresses. Based on feature analysis, a new health indicator of the mean of the nth order peaks is proposed to detect progressive localized tooth spalling. Results indicate that the proposed indicator shows very good performance for tracking the severity of progressive tooth spalling under different speed conditions.