Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System

Abstract

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions.

Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory

“…A system is first developed for the basic product, and then additional systems for new products are created by fine-tuning the parameters of the existing system. Mazzoleni et al [13], for instance, investigated the application of fuzzy logic and transfer learning to industrial environments without labeled data, while Raouf et al [14] considered industrial robots working in different environments. The application of transfer learning to upgrade existing systems is favored by factories because it requires the least financial and time investments.…”

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

“…Lee et al [65] have proposed a fault detection approach for the robotic servo-motor under varying working conditions. Rauf et al [66] have proposed a transfer learning-based DL approach for fault detection in the industrial robotic system. Zhou et al [67] have proposed a harmonic reducer fault diagnosis using the deep learning-based model.…”