A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing

Leoni, Leonardo; Carlo, Filippo De; Abaei, Mohammad Mahdi; BahooToroody, Ahmad

doi:10.1016/j.compind.2022.103645

Cited by 5 publications

(3 citation statements)

References 48 publications

(55 reference statements)

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“…CBM is a strategy that is based on condition monitoring of equipment over its operations, enabling just-in-time maintenance [8]. According to Leoni et al [9], one of the main advantages over preventive maintenance strategies is that it can be scheduled only based on the condition of the equipment, with no need to plan interventions based on reliability parameters. Lee et al [10] mention that although implementing a CBM strategy is more difficult and costly, it ultimately leads to less wasted equipment life, reducing costs associated with production, parts stock, number of personnel, tools, etc.…”

Section: Condition-based Maintenance-cbmmentioning

confidence: 99%

“…CBM is used in smart factories through the use of IoT systems, CPS (cyberphysical systems), sensor technology and AI technologies. In this way, the collection and processing of large quantities of data (big data) can be done in real time [9,10,27,28].…”

Section: Condition-based Maintenance-cbmmentioning

confidence: 99%

“…Summarizing, CBM can be divided into three phases [9,10,18,34]: (i) Data acquisition, where useful data are collected, usually from sensors;…”

Section: Condition-based Maintenance-cbmmentioning

confidence: 99%

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Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

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The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

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Section: Condition-based Maintenance-cbmmentioning

confidence: 99%