Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

Rivas, Andy; Delipei, Gregory; Hou, Jason

doi:10.1016/j.pnucene.2022.104143

Cited by 15 publications

(3 citation statements)

References 15 publications

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“…Table 4 lists typical parameters for equipment, control, and operation across various industrial processes. energy thermoelectric generator design [120] component remaining useful lifetime [121] agriculture optimization on hydraulic components [122] control automotive longitudinal control of vehicles [123] chemical model predictive control [124] construction supply air flow rate and temperature [125] operation environment filtrate flux operating conditions [126] energy hydrogen production [127] indirect air-cooling power units [128] By analyzing vast datasets and adapting to evolving scenarios, ANNs can identify patterns and make informed decisions. In smart manufacturing, ANNs have diverse applications, including predictive maintenance, where ANNs utilize sensor data to anticipate machine failures and schedule maintenance, enhancing reliability.…”

Section: Machine Learningmentioning

confidence: 99%

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Lee

2023

Processes

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In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

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Section: Machine Learningmentioning

confidence: 99%

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Lee

2023

Processes

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“…The method using machine learning algorithms such as regression trees and random forests is called data-driven model predictive control (DPC), which aims to improve robustness to uncertainties in real data collection and weather forecasting. Rivas et al [39] adopted a Bayesian neural network (BNN) to predict remaining useful life (RUL) and its uncertainty for the effective predictive maintenance of equipment health, contributing to minimizing the cost and number of unplanned maintenance operations. Siryani et al [40] employed a Bayesian belief network to enhance the cost efficiency of complex systems of public utility network operation and maintenance life cycle.…”

Section: Intelligent Operation and Maintenance And Dtmentioning

confidence: 99%

Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework

Jiao

Liu

et al. 2023

Sensors

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Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional ‘factor-energy consumption’ to three parts of ‘factor-event-energy consumption’, which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

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“…CBM is used in prognostics and health management (PHM), an engineering discipline which studies the health state of equipment and predicts its future evolution with the integration of aspects such as logistics, security, reliability, mission criticality and cost-effectiveness; thus, PHM goes beyond CBM [18] [19]. There are three main methods for implementing CBM [20]: physical model-based approaches [21], data-driven approaches or surrogate models [22], and hybrid modelbased approaches (HyMA) [23]. Physical model-based approaches are based on mathematical models of the physical system; if the system degradation is accurately modelled, these approaches tend to be more effective than other approaches [20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

2022

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Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components' degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate runto-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components' health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter's run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.INDEX TERMS Prognostics and health management, Hybrid Modelling, Deep learning, HVAC system, railway.

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Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

Cited by 15 publications

References 15 publications

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

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