Deep learning and its applications to machine health monitoring

Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Applications of machine learning to machine fault diagnosis: A review and roadmap

Deep learning for smart manufacturing: Methods and applications

https://escholarship.org/content/qt5ns8r3fs/qt5ns8r3fs.pdf?t=pihyfp

Remaining useful life estimation in prognostics using deep convolution neural networks

In traditional supervised methods, model training requires a large number of manual labeled samples, which are time-consuming and labor-intensive. A new unsupervised method for rolling bearing fault diagnosis based on clustering and stacked auto-encoder is proposed in this paper. The new method provides a high-efficiency model, which consists of three parts. Raw vibration signals are extracted into multidimensional feature matrix, of which the dimension is further reduced by laplacian eigenmaps (LE) algorithm. The reduced feature matrix are then clustered using a K-means algorithm to form the training set, which are the inputs into a stacked auto-encoder (SAE). By fine tuning the parameters of the deep network, the trained SAE is employed to classify fault type and fault size. The experiment uses the proposed method to perform fault diagnosis from the data of rolling bearing system, and the results show that the method is able to distinguish different fault categories and sizes accurately and effectively. More importantly, it indicates that the proposed method removes the initial steps of artificial calibration samples and thus improves the intelligent level.

An Unsupervised Method for Rolling Bearing Fault Diagnosis Based on Clustering and Stacked Auto-Encoder

Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions

This paper is concerned with the interval observer for switched system. The frame of interval observer is designed by the theory of positive system and Lyapunov stability, and the aim is to achieve both the positivity and exponential stability of the corresponding error system. Different from the current works, multiple linear copositive Lyapunov function is employed for stability analysis and the sufficient conditions for the existence of the interval observer are derived by linear programming forms. Finally, two numerical examples are provided to show the effectiveness of the designed observer.

Interval observer design for switched systems by linear programming method

Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects.

Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions

Planetary gearsets play an important role in the transmission system. The kinematic analysis of transmissions seems to be particularly important. In this paper, a novel and simplified method based on matrix is put forward. By deriving the velocity constraint equations, the equations for speeds, torques, and accelerations are solved in the expression of matrix. This method greatly simplifies the modeling method of transmissions in the vehicles, and can be further used in the dynamic simulation.

Liang Chen

Papers

An Unsupervised Method for Rolling Bearing Fault Diagnosis Based on Clustering and Stacked Auto-Encoder

Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions

Interval observer design for switched systems by linear programming method

Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions

A novel method for modeling and kinematic analysis of transmissions