In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

A systematic review of deep transfer learning for machinery fault diagnosis

A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.

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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/ .

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Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

Bearing fault diagnosis has attracted increasing attention due to its importance in the health status of rotating machinery. The data-driven models based on deep learning (DL) have become more and more intelligent in the field of fault diagnosis, and among them convolutional neural network (CNN) has been widely used in recent researches. However, traditional CNN is not easy to capture right fault features due to their fixed geometric structures, especially under complex working conditions in fault diagnosis. To address these challenges, we propose a novel model by combining InceptionResnetV2 with Deformable Convolution Networks, named DeIN. We replace the basic form of convolution with deformable convolution in specific layers, and a main classifier and an auxiliary classifier are designed to output the classification result of our proposed model, to adapt to the non-rigid characters and larger receptive field in time-frequency graph (TFG). Experimentally, the one-dimensional signals are transformed into TFGs and as input of the proposed model, and this aims to find useful features during the training process. To verify the generalization ability of the proposed model, we apply a set of cross-over tests based on two popular datasets, and our model achieved 99.87% and 94.52% highest-precision fault classification results comparing with other state-of-the-art CNN models.

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A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.

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A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring

Diagnosing the faults of rolling bearing is an important thing, because it is the key component as well as the most easily damaged part in mechanical equipment Whereas the traditional intelligent diagnosis methods rely heavily on the complex time-consuming feature extraction processes and rich diagnostic experience Using the characteristics of one-dimensional vibration signals, we propose a one-dimensional residual convolution neural network (1D-RCNN) that directly uses time-domain waveforms as input By introducing 1D CNN, residual blocks, batch normalization, and global average pooling layer, our model can effectively extract and identify features through deep convolution neural networks automatically without overfitting A series of experiments were conducted based on the dataset of Case Western Reserve University (CWRU) to evaluate the performance of our proposed approach According to the results, the proposed method is superior to state-of-the-art approaches using handcrafted features or raw data as input, and has certain practical potential in industrial production

A Novel Bearing Fault Diagnosis of Raw Signals Based on 1D Residual Convolution Neural Network

The invention discloses a detachable crane type crane. The crane comprises a stainless steel tube support, a stainless steel tube hinge frame, a lifting hook, a steel wire rope, a nylon rope and a winding mechanism. The stainless steel tube hinge frame is hinged to the stainless steel pipe support; the lifting hook is connected to one end of steel wire rope, the steel wire rope is connected to thewinding mechanism after bypassing the cantilever end on the stainless steel tube hinge frame and a fixed pulley mounted inside; the winding mechanism is installed in the stainless steel pipe support,one end of the nylon rope is connected to the stainless steel tube hinge frame, the rotary ratchet self-locking mechanism is installed at the hinged position of the stainless steel tube hinge frame and the stainless steel tube support, and the other end of the nylon rope is connected with the pull ring. On the basis of an original lifting mechanism, the safety of the crane is greatly improved bymeans of the principle of a crane type crane, and meanwhile, the convenience of actual use is improved. The crane has the characteristics of a crane type structure and detachable assembly, solves theproblem of heavy object lifting in various complex environments where heavy objects are inconvenient to lift, enables equipment to be more stable and easier to operate, and greatly improves safety.

Wanli Yang

Papers

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring

A Novel Bearing Fault Diagnosis of Raw Signals Based on 1D Residual Convolution Neural Network

Detachable crane type crane