A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lifespan; second, the probability of various faults varies, both of which result in an imbalance in the data. The consequence of data imbalance in intelligent fault diagnosis methods has attracted extensive attention from the research community, and a significant number of papers have been published. Nevertheless, a comprehensive review of achievements in this field is still missing, and the research perspectives have not been thoroughly investigated. To end this, we review and discuss all the research achievements in fault diagnosis under data imbalance in this paper, based on our best knowledge. First, the existing imbalanced learning methods are classified into three categories: data processing methods, model construction methods, and training optimization methods. Then, the three methodologies are introduced and discussed in detail: the data processing method is to optimize the inputs of the intelligent fault diagnosis model so that the imbalance rate of the sample set involved in training is reduced; the model construction method is to design the structure and the features of the intelligent fault diagnosis model so that the model itself is resistant to the effects of imbalance; the training optimization method is an optimisation of the training process for intelligent fault diagnosis models, raising the importance of the minority class in the training. Finally, this paper summarizes the prospects of the imbalanced learning problem in intelligent fault diagnosis, discusses the possible solutions, and provides some recommendations. 

A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis

Bearing faults are critical in machinery; their early detection is vital to prevent costly breakdowns and ensure operational safety. This study presents a pioneering take on addressing the challenges of imbalanced datasets in bearing fault diagnosis. By mitigating issues such as mode collapse and vanishing gradients, our novel method employs Conditional Generative Adversarial Networks (CGANs) with spectral normalization and adaptive adversarial noise injection to generate high-quality bearing fault samples. This enhances generalization and robustness against noisy data, significantly improving the stability of CGAN training. To extract meaningful features from grayscale bearing fault images, we introduce a novel combination of involution and convolution feature extraction method named I-C FFN. This innovative feature extraction method captures both local and global information, making it capable of handling various types of features, including channel-agnostic, spatial-specific, spatial-agnostic, and channel-specific characteristics. Our proposed oversampling methodology helped enhance the performance of proposed classification scheme as well as of benchmark transfer learning models. Having accuracy values between 99.40% to 99.61% for 0 and 1 HP imbalanced dataset respectively, our models outperformed standard transfer learning methodologies. Furthermore, by the inclusion of our proposed Adaptive Adversarial Class-Conditional GAN (AAC-cGAN), the samples quality and the robustness to noise was significantly increased as demonstrated by the quantitative assessment through various Evaluation Metrics employed in this paper. Lastly, the performance of each combination of both the up-sampled and under-sampled methodologies were assessed through multiple metrics to determine the effectiveness of our proposed approach in addressing class imbalance in bearing fault diagnosis. 

Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction with Adversarial Noise Injection in Conditional GANs

Rotating machinery often works under time-varying speeds, and nonstationary conditions and harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods, including nonstationary signal processing methods and data-driven methods, have been developed. This article presents a comprehensive review of the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation, and generalized demodulation, are reviewed. Second, the time–frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction- and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed. We not only review the relevant state-of-the-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages and the challenges that will be encountered when applying them to industrial applications. This article is expected to provide new graduate students, institutions, and companies with a preliminary understanding of the methods on this topic.

Rotating Machinery Fault Diagnosis under Time-Varying Speeds: A Review

Abstract The term “over-skidding” indicates that the cage rotational speed ratio exceeds the theoretical value as ball purely rolls on the raceway. Different from the skidding phenomenon that occurs in low-load and high-speed bearing, over-skidding usually occurs in large-size angular contact bearings, and it is still difficult to suppress under high load conditions. The main forms of damage to the raceway by over-skidding are spinning and gyro slip. To further explore the vibration characteristics and thermal effects of this phenomenon, a set of over-skidding tests of an angular contact bearing with a bore diameter of 220 mm were conducted on an industrial-size test bench. Through the experiment, the influence of axial load, rotational speed, and lubrication conditions on the occurrence of over-skidding were determined. Based on a previous dynamics model, the heat generation and thermal network models were integrated in the present study to predict the over-skidding and its thermal behavior. The model was validated in terms of the measured degree of over-skidding and temperature rise. The results showed that the degree of over-skidding reaches up to 12% of the theoretical value, and the friction power loss of the ball-pocket accounts for 30% of the total power loss. The analysis of the vibration signal showed a strong correlation between the bearing vibration characteristics and over-skidding behavior, thereby providing a way to indirectly measure the degree of over-skidding. 

/pdf/dynamic-thermal-and-vibrational-analysis-of-ball-bearings-21yijemo.pdf

Dynamic, thermal, and vibrational analysis of ball bearings with over-skidding behavior

The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, and small and imbalanced (S & I) fault samples are common, which seriously reduces the accuracy and stability of the fault diagnosis model. To solve this problem, an auxiliary classifier generative adversarial network with spectral normalization (ACGAN-SN) is proposed in this paper. First, a generation module based on a deconvolution layer is built to generate false data from Gaussian noise. Second, to enhance the training stability of the model, the data label information is used to make label constraints on the generated fake data under the basic GAN framework. Spectral normalization constraints are imposed on the output of each layer of the neural network of the discriminator to realize the Lipschitz continuity condition so as to avoid vanishing or exploding gradients. Finally, based on the generated data and the original S & I dataset, seven kinds of bearing fault datasets are made, and the prediction results of the Bi-directional Long Short-Term Memory (BiLSTM) model is verified. The results show that the data generated by ACGAN-SN can significantly promote the performance of the fault diagnosis model under the S & I fault samples.

/pdf/a-novel-method-for-fault-diagnosis-of-bearings-with-small-4dy7kz18.pdf

A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

https://www.mdpi.com/1996-1073/14/4/1079/pdf

An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

The transient thermal performance of rolling bearings affects the mechanical performance and system safety of traction motors Most of the traditional empirical formulas used in temperature analysis have been simplified and cannot be completely applied to the calculation of heat generation and convection heat transfer coefficients Based on the comparative analysis of finite element transient temperature and experimental data, this paper proposes a correction method of mathematical model and derives an accurate calculation formula for the heat generation and lubricant convection heat transfer coefficient of ball bearings applicable for the non-driving end in the traction motor of a high-speed EMU (Electric Multiple Unit) The accuracy of the results has been verified by durability experiment data In addition, with changes in speed, radial load and other factors taken into account, we have analyzed the influence of these time-varying factors on ball bearing temperature, as well as the temperature distribution law of each component in a grease-lubricated bearing, in a bid to lay a foundation for follow-up research on the heat transfer laws of traction motors and rolling bearings

Study on the Thermal Performance and Temperature Distribution of Ball Bearings in the Traction Motor of a High-Speed EMU

Intelligent fault diagnosis of rolling bearings under non-stationary and time-varying speed conditions is still a challenging task. At the same time, a reasonable explanation for an intelligent diagnosis model based on features is currently lacking. Therefore, we exploit an explainable one-dimensional convolutional neural network (1DCNN) model by combining with the demodulated frequency features of vibration signals and apply it to the fault classification of rolling bearings under time-varying speed conditions. First, the speed signals obtained by the speed encoder were transformed into generalized demodulation operator (GDO). Second, combined with the sensitive frequency band and GDO, the generalized demodulation algorithm was used to extract the frequency features from the amplitude envelope of the vibration signal. Subsequently, the proposed lightweight 1DCNN was trained to classify the frequency features and identify the health states of the rolling bearing. Finally, the local interpretable model-agnostic explanations model was utilized to explain the proposed model based on the features which own weight. It is found that the internal classification mechanism of the lightweight 1DCNN is realized according to the distribution of fault features, which is consistent with the process of human brain analysis. Two kinds of time-varying speed datasets which come from the University of Ottawa and XJTU are tested and verified. The results show that compared with other intelligent fault diagnosis methods, the identification error of the proposed method is lower and the diagnosis stability is better. The average diagnostic accuracy was 96.26% and 99.82%, respectively.

Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions

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