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.

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.