A comprehensive framework from real‐time prognostics to maintenance decisions

Jain, Amit Kumar; Dhada, Maharshi; Hernández, Marco Pérez; Herrera, Manuel; Parlikad, Ajith Kumar

doi:10.1049/cim2.12021

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…This study contains usable ideas such as data processing and arranging operations in the system. The fourth, a new comprehensive maintenance support system, was proposed by (Jain et al, 2021). In addition, Weibull developed a survival theory-based real-time prognostics module named (WTTE-RNN).…”

Section: Time Series Forecastingmentioning

confidence: 99%

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Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Bahar,

Schokry,

Alhanjouri

2024

PJBAS

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Predictive maintenance (PdM) appears to predict faults before they occur because unexpected industrial equipment failures directly affect workers' safety, cost, and work continuity. In this context, developing a PdM system needs historical data for failures, but those data are not available in our case, which is a sewage centrifugal pump, where failures had not been recorded before. Therefore, this research aims to develop a system for PdM that works efficiently in real-time and does not need historical data for failures; it can also predict failures at different periods and conditions. The "Data-Driven" method is a suitable methodology to apply to conditions data; also, time series forecasting and anomaly detection (AD) are the most applicable models for the studied case. The Main Bearing Temperature was chosen in the models because it is the most applicable parameter. The models' performance was evaluated in two ways: accuracy and resource consumption (execution time and RAM). After that, the most important accuracy metrics are a root mean square error (RMSE) for forecasting models and excess Mass and Mass Volume for AD models. The experimental results presented "TBATS" as the best forecast model and QuantileAD from the "ADTK" model as the best AD method.

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Section: Time Series Forecastingmentioning

confidence: 99%

“…(Lin et al, 2019) studied data are close to this research, so their models will be better examined. The Studies (Amihai et al, 2018;Fernandes et al, 2020;Jain et al, 2021) include data processing techniques and the design of framework processes that are beneficial for the research.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Bahar,

Schokry,

Alhanjouri

2024

PJBAS

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“…[26] presented an extensive analysis of using WTTE-RNN for prognosis of simulated turbofan failures by comparing WTTE-RNN with other standard prognosis techniques and concluded that WTTE-RNN was the most flexible and accurate. Other applications of WTTE-RNN for failure prediction include [1,15,16].…”

Section: Weibull Time-to-event Recurrent Neural Networkmentioning

confidence: 99%

Weibull recurrent neural networks for failure prognosis using histogram data

Dhada

Parlikad

Steinert

et al. 2022

Neural Comput & Applic

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Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.

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“…Present work considers the joint problem of job sequencing and inventory control for a complex multi-machine system with stochastic parameters, which significantly increase the problem complexity. Researchers generally use the simulation-based method to solve such problems (Garg and Deshmukh 2006; Sharma, Yadava, and Deshmukh 2011,Jain et al, 2019). Thus, a simulation-based method approach is used in this research to solve the problem.…”

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confidence: 99%

Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing

Kumar

Petchrompo

Ahmed

et al. 2022

Lecture Notes in Mechanical Engineering

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Digitalization and Industry 4.0 have enabled real time interaction of customers and suppliers with manufacturing systems. However, this requires a high level of reactivity in the value chain to sustain in a competitive economy. As a result, Quick Response Manufacturing (QRM) in dynamic conditions such as wide product variety, change in demand, volatile market conditions, uncertainty in supply, machine failures, etc. is critical in capturing the benefits of digitalization in industries. To empower QRM, Paired-cell Overlapping Loops of Cards with Authorization (POLCA) is suitable for a manufacturing environment with a wide variety and highly dynamic conditions. The combination of QRM and POLCA provides a significant competitive advantage through their quick response to dynamic conditions. Such a combination is not reported in the literature. Accordingly, this paper proposes a simulation-based POLCA integrated with the QRM approach for smart manufacturing. The offered approach evaluates a dynamic sequence of jobs at each machine to meet the due date and at the same time control the material flow to improve system performance. The approach is illustrated with the help of a complex manufacturing scenario. A simulation-based technique is used to evaluate the system performance. Further, the performance of the approach under dynamic conditions is analyzed. Results show that improved system performance can be achieved by considering QRM and POLCA in dynamic conditions.

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A comprehensive framework from real‐time prognostics to maintenance decisions

Cited by 8 publications

References 26 publications

Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Weibull recurrent neural networks for failure prognosis using histogram data

Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing

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