Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks. (March 2023)

• Record Type: Journal Article
• Authors: Shaheen, Basheer; Kocsis, Ádám; Németh, István
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Machine learning -- Fault prognostics -- Remaining useful life prediction -- Accumulative neural networks -- Predictive maintenance -- Maintenance planning and scheduling; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105749
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: It is necessary to develop advanced fault prognostics techniques for improving maintenance planning and scheduling to avoid unexpected shutdowns, additional expenses and decreased productivity. Such techniques include advanced failure prediction and remaining useful life (RUL) estimation of mechanical components comprising manufacturing systems with complicated structures. This study proposes a novel data-driven prognostic analysis approach for predicting the failure of a mechanical component based on its degradation path and estimating the RUL. A simulated labelled degradation dataset of a mechanical component with a predefined failure threshold was exploited. In order to increase the ability to maintain the increasing trend and the monotony of the degradation path, supervised machine learning models, including combined artificial neural network architectures and an improved version of the neuron-by-neuron training algorithm using accumulative neural networks design were applied for the prediction process. The expected degradation path was extrapolated as a testing dataset of the trained prediction model using an accumulative function. The predicted values are updated with each new data point during the training process until a failure occurs. The results show that the used approach is efficient in predicting the failure and estimating the RUL of mechanical components with high accuracy and a high prediction success rate, and it can maintain the monotonous trend of the degradation path. On the other hand, the used network architectures enable the prediction of the failures of mechanical components within a manufacturing system having a complex structure and providing a vast amount of data. Highlights: A novel methodology for fault prognostics of mechanical components. Improving the ANN-NBN prediction model with accumulative network design. Improving predictability and performance by utilising FCN and ACN architectures. A feasible tool for RUL predictions of mechanical components.