Digital twin-driven machine learning: ball bearings fault severity classification

Abstract

Machine learning algorithms (MLAs) are increasingly being used as effective techniques for processing vibration signals obtained from complex industrial machineries. Previous applications of automatic fault detection algorithms in the diagnosis of rotating machines were mainly based on historical operating data sets, limiting the diagnostic reliability to devices with an extended operating history. Moreover, physically collected data are often restricted by the conditions of acquisition and the specific elements for which they were recorded. Digital twin (DT) provides a powerful tool able to generate a huge amount of training data for MLAs. However, the DT model must be accurate enough to substitute the experiments. This work aims to escape the experience requirement by using a simulation-driven MLA based on the multifactorial analysis of fault indicators associated with a DT. To achieve this approach, a numerical model of a rotor-ball bearing system is developed. The latter is updated according to a parameter update scheme based on a comparison between the relevant features of the experimentally measured signals and the signals simulated by the model. These features are chosen as the selected input parameters of the MLA classifier. The results show that after updating, the developed DT has provided a reliable diagnostic with an adaptive degradation analysis, which makes the simulated data suitable for the construction of a machine learning predictive model. Two common MLAs, (multi-kernel support vector machine) and (k nearest neighbor’s algorithm), were trained using the simulated data and validated later against experimental datasets.

Order-Based Identification of Bearing Defects under Variable Speed Condition

“…Based on an order analysis technique, the three features associated with bearing defects: SPRO, SPRI, and SPRR are updated to perform with non-stationary signals. These are extracted from the considered vibration signals and used to build a predictive classification model for the MLA-classifier: MSVM [8]. The constructed classification model has proven to be effective in the identification of bearing defects under variable speed conditions, confirming the performance of the proposed features.…”

“…Hotait et al updated the unsupervised classifier Optics (Ordering Points to Identify the clustering structure) to provide real-time detection of bearing defects based on vibration monitoring. Farhat et al [8] used two supervised machine learning classifiers: Multi kernel support vector machines (MSVM) and K-nearest neighbors (KNN), to determine the severity of a bearing outer race defect in a shaft bearing system. In many other studies [9][10][11], several kinds of MLAs are also applied to automate the detection and/or the characterization of the severity of bearing defects.…”

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

“…Across the reviewed literature, researchers have employed the whole array of ML algorithms types in their DT implementations: traditional ML [74], Deep Learning (DL) [75], supervised ML [76], unsupervised ML [77], classification ML [78], regression ML [79], Reinforcement Learning (RL) [80], etc.…”

State Perception and Prediction of Digital Twin Based on Proxy Model

“…During the usage of mechanical components, damages and even failures can occur due to external and internal factors, making it difficult to maintain a stable operating state over a long period of time [1]. As components are used over an extended period, the operating environment becomes increasingly complex, posing new challenges for equipment diagnosis and maintenance [2][3][4]. Currently, the maintenance of mechanical components is mainly based on after-the-fact maintenance and limited experience-based predictive maintenance, which limits the accuracy and effectiveness of component maintenance and can affect the operation of the entire equipment [5].…”