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A Methodological Framework for the Prediction of Quality and Remaining Useful Life of Industrial Components and Systems

Hosseinpour, Fatemeh | 2024

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  1. Type of Document: Ph.D. Dissertation
  2. Language: English
  3. Document No: 57964 (08)
  4. University: Sharif University of Technology and Milan University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Behzad, Mehdi; Baraldi, Piero; Zio, Enrico
  7. Abstract:
  8. Recent advancements in sensors and network technologies have led to a significant increase in the availability of data, collected during the entire life cycle of industrial components, from the production phase to field operation. This PhD thesis considers time series of measurements of different signal types, such as vibration, temperature, and pressure and other signals, to enhance the reliability of industrial components. Specifically, the research considers the two most critical phases of the component life-cycle: the early-life phase, during which failures are typically due to manufacturing defects caused by low production quality, and the wear-out phase, during which failures are due to component degradation. In this context, a framework is developed for constructing indicators of abnormality behaviour to estimate the production quality and to predict the Remaining Useful Life (RUL) of industrial components. Specifically, two fundamental tasks are considered: a) the assessment of the production quality by monitoring the production process and b) fault prognostics, which aims to reduce failures, by predicting the future evolution of the degradation of the component and its RUL. The framework is based on the use of Machine Learning (ML) for processing the time series data and Particle Filters (PF) for combining data with physics-based models of the degradation process. With respect to task a), the main challenges are: 1) to process multi-dimensional time-series data of raw signal measurements characterized by highly nonlinear dynamic behaviors; 2) the lack of data labeled with the component state (normal/ abnormal conditions), as the ground truth state of the components is typically unknown; and 3) the scarcity of failure data due to the high-quality standards in several manufacturing industries. To address these challenges, the PhD thesis develops novel unsupervised methodology for the detection of the occurrence of abnormal conditions during component production and the estimation of the component quality. It consists of k-fold cross-validation, Long Short-Term Memory (LSTM) autoencoders, and Mahalanobis distance-based abnormality detection. The proposed methodological framework has been applied to three case studies from the semiconductor industry. The obtained results demonstrate the superior performance of the proposed method compared to the state-of-the-art, enable the identification of low-quality components before they start operation, and allow for optimizing decisions about the Burn-In (BI) policy to be applied to the production lots. With respect to task b), the main challenges to be addressed are: 1) the scarcity of data for training data-driven models and the difficulty in generalizing them for applications in similar systems, as most of the data are collected from run-to-failure laboratory tests; and 2) the complexity involved in developing physics-based models of the degradation mechanism. A new framework defines abnormality indicators of components from information extracted from time-series data using a plethora of methods, including clustering methods, also based on the use of the Manhattan distance, and deep learning methods in conjunction with physics-based models. A PF method combines Monte Carlo simulations with a degradation model to estimate RUL and quantify the associated uncertainties. The proposed methodological framework has been applied to three benchmark case studies based on the IMS, PROGNOSTIA and SUT bearing datasets. The accurate RUL estimations obtained by the developed method can contribute to the deployment of predictive maintenance in industry, which can lead to reduce components failures and maintenance costs and increase production availability
  9. Keywords:
  10. Quality Control ; Prognostics Failure ; Remaining Useful Life ; Unlabeled Data ; Machine Learning ; Physics-Based Machine Learning ; Long Short Term Memory (LSTM) ; Particle Filter ; Multi-Dimensional Time-Series ; Abnormality Indicator

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