Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

Abstract

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.

Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

“…After adjusting the sample weight, the sampling training process using the improved LM algorithm is shown in Figure 3. From Figure 4, when the unimproved neural network is trained 100 times, there is still a big gap from the error of 10 -2 , and the neural network combined by the SOM method and the LM method, convergence is reached after 20 trainings [52][53][54][55][56][57][58] . Using the above combination of SOM method and LM method, the training process is shown in Figures…”

Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples

“…However, negative transfer may occur if source and target domains have significant differences; i.e., if domain-specific samples have large discrepancies in their probability distributions [18]. In other words, bringing source domain knowledge into the target task does not help, but rather hinders performance.…”

“…In other words, bringing source domain knowledge into the target task does not help, but rather hinders performance. The effect of negative transfer has been noted in several transfer-learning-enabled fault diagnosis applications [15,18,19], inspiring researchers to examine the causes of negative transfer. A review can be found in [20,21].…”

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

“…For example, Zhou et al [21,22] proved that it may be better to ensemble many instead of all of the neural networks and decision trees from the point of view of regression and classification, and proposed an algorithm called GASEN and GASEN-b, which uses a genetic algorithm to improve weights. Lee et al [23] proposed a multi-objective instance weight transfer learning network to solve the problem of fault diagnosis. The effectiveness of the proposed algorithm was verified by industrial robot and spot-welding experiments.…”