Sparse transfer learning for identifying rotor and gear defects in the mechanical machinery

Kumar, Anil; Vashishtha, Govind; Gandhi, C.P.; Tang, Hesheng; Xiang, Jiawei

doi:10.1016/j.measurement.2021.109494

Cited by 35 publications

(17 citation statements)

References 51 publications

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“…This approach has the advantage that deeper layers that generate deeper features can be adopted in the target domain. The following applications for condition diagnosis can be found in the literature: bearings [66], [99], [100], [101], aircraft engines [102], quadrotor drones [103], batteries [88], [104], [105], gas turbines [106], tanks [107], and gearboxes and rotors [108]. Xu et al [23] transferred the parameters of shallow CNNs trained with source data to a deeper CNN, which was then fine-tuned with target data.…”

Section: A Parameter Transfer Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: A Parameter Transfer Approachesmentioning

confidence: 99%

“…Gearboxes are one such example. Kumar et al [108] utilized a bevel gearbox dataset as similar system data for spur gearboxes from the IEEE PHM Challenge Competition 2009. The transfer between bearings and gearboxes already listed in the course of the bearing applications should also be mentioned [24].…”

Section: B Similar Gearboxes and Other Rotating Componentsmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…The performance of data-driven methods could be poor with few labeled training samples [23,24]. To solve this problem, transfer learning (TL) has been developed with a small labeled sample in the target domain [25][26][27]. Li et al proposed a partial domain adaptation method to achieve fault diagnosis [28].…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

et al. 2022

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Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

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“…Their method identifies gear tooth form defects. Ku et al [12] proposed a sparse deep learning model in the absence of training samples and achieved good classification results on both gear and rotor datasets. Therefore, in the defect detection algorithm, the deep learning algorithm based on convolutional neural network often has a better performance in the case of a large sample data volume, and it can complete the detection of the target defect on the basis of the self-extraction of the feature, which is the current mainstream algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Gao

Zhang

et al. 2022

Applied Sciences

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Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small.

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Sparse transfer learning for identifying rotor and gear defects in the mechanical machinery

Cited by 35 publications

References 51 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

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