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In the current Industrial 4.0 revolution, Prognostics and Health Management(PHM) is an emerging field of research. The difficulty of obtaining data fromelectromechanical systems in an industrial setting increases proportionallywith the scale and accessibility of the automated industry, resulting in a lessinterpolated PHM system. To put it another way, the development of an accuratePHM system for each industrial system necessitates a unique dataset acquiredunder specified conditions. In most circumstances, obtaining this one-of-a-kinddataset is difficult, and the resulting dataset has a significant imbalance, alack of certain useful information, and multi-domain knowledge. To addressthis, this paper provides a fault detection and diagnosis system that evaluatesand pre-processes Imbalanced, Scarce, Multi-Domain (ISMD) data acquired from anindustrial robot utilizing Signal Processing (SP) techniques and DeepLearning-based (DL) domain knowledge transfer. The domain knowledge transfer isused to produce a synthetic dataset with a high interpolation rate thatcontains all the useful information about each domain. For domain knowledgetransfer and data generation, Continuous Wavelet Transform (CWT) withGenerative Adversarial Network (GAN) was used, as well as Convolutional NeuralNetwork (CNN) to test the suggested methodology using transfer learning andcategorize several faults. The proposed methodology was tested on a realexperimental bench that included an industrial robot created by HyundaiRobotics Co. This development resulted in a satisfactory resolution with 99.7%(highest) classification accuracy achieved by transfer learning on several CNNbenchmark models.

Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce,  Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health  Management (PHM)