A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

Abstract

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions.

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

“…Faulty bearings, mechanical seal related faults, and impeller defects are the primary reasons for catastrophic failure of centrifugal pumps [2]. Numerous studies have been conducted to identify bearing defects [3]- [6]. However, very few research studies are available on the diagnosis of mechanical seal and impeller defects.…”

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

“…Additionally, it must be mentioned that others have tried to overcome these challenges through ensemble learning, parallel Neural Networks, and transfer learning-based solutions; however, complex condition monitoring structures are needed to achieve good performance results. In this regard, for example, in [26], a diagnosis method that first includes a higher-order spectral analysis and multitask learning-based convolutional neural network is proposed for the identification of the bearing health condition, and second, identifying bearing fault conditions by means of a transfer learning-based approach in the presence of multiple crack conditions. However, although this proposal leads to the efficient diagnosis of bearing faults, prior knowledge about the implementation and configuration of deep learning (DL) techniques is the main limitation to develop complex structures that are also based on ensemble learning approaches [27].…”

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

“…Chen et al [18] proposed an unsupervised domain adaptation method which could maximize the mutual information between the target feature space and the entire feature space and minimize the featurelevel discrepancy between the two domains. Hasan et al [19] proposed a multitask-aided transfer learning-based diagnostic framework. This method applies multitask learning-based convolutional network to identify working conditions, and then identifies health status of the rolling element bearings based on transfer learning.…”