Abstract
In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, convolutional neural network-based deep neural network techniques are investigated for the remaining useful life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pretrained models, using a classic machine learning approach, i.e., support vector regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE = 0.058) to that of transfer learning, which, instead, remains at a lower or slightly higher level (MAPE = 0.416) than support vector regression (MAPE = 0.857).
Highlights
Over recent years, asset maintenance has received increasing attention in the literature
The subsections deal with the following aspects: (1) the data acquisition system and experimental setup, (2) the 3D scanning process, (3) the adopted metrics to evaluate remaining useful life (RUL) prediction performance, (4) the deep neural networks (DNNs) architectures generated by genetic optimization, (5) the genetic optimization technique, and (6) the support vector regression (SVR) approach used as classic machine learning (ML) benchmark
A DNN-based RUL prediction framework for a punch tool, whose deterioration is due to surface deformation, was investigated
Summary
Asset maintenance has received increasing attention in the literature. If considering that appropriate maintenance management has a direct effect on reducing costs and increasing system reliability, availability, and safety [1], it is easy to understand how asset maintenance is a critical success factor in the current industrial revolution 4.0 [2]. With the main objective of reducing unexpected breakdowns and possibly catastrophic consequences, maintenance strategies can be roughly classified under [4]: (1) corrective maintenance, (2) preventive maintenance, and (3) prognostics and health management (PHM). The preventive maintenance aims to prevent the aforementioned problems by scheduling inspection and repair interventions at regular time intervals or operation cycles. In this case, the bigger downside is the waste of time and the replacement costs for components that often are still working. PHM relies on the continuous monitoring of equipment health conditions to predict the degradation status, in terms of remaining useful life (RUL), supporting more accurate maintenance management decisions
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