A review of the role of prognostics in predicting the remaining useful life of assets

Roman, Darius; Dickie, Ross; Flynn, David S.; Robu, Valentin

doi:10.1201/9781315210469-116

Cited by 10 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To satisfy the uncertainty estimation requirement we employ a Bayesian-based algorithm to solve equation 3, namely Gaussian process regression. Bayesian based algorithms including relevance vector machine (RVM) as formulated by Tipping [35] and Gaussian process regression (GPR) have been used for battery capacity estimation [36], [24]; however none have been used for EC capacitance fade degradation. We chose GPR as the primary algorithm due to its non-parametric properties and its ability to provide predictive distributions for test cases.…”

Section: Machine Learning Degradation Modelmentioning

confidence: 99%

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

View full text Add to dashboard Cite

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturerspecified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

show abstract

Section: Machine Learning Degradation Modelmentioning

confidence: 99%

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To support the continuity of reliable, affordable and increasingly more sustainable electrical energy, network reinforcement may be required and this represents a significant cost to network operators and consumers. For example, modernisation of electrical networks in the United States is expected to incur a $3 trillion cost, which excludes generation asset investment [12]. To support the deferment of network investment, and to ensure continuity of reliable service within a modernised electrical network, asset management has a strategic role.…”

Section: Motivation and Related Work A The Importance Of Electrical Asset Monitoringmentioning

confidence: 99%

Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management

Odo

McKenna²,

Flynn

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Automated visual detection and analysis of U-bolts has not been previously reported. We demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components.

show abstract

“…Statistical analysis often ignores the mechanism of failure or assumes only one failure mechanism, which is not the case in many complex engineering systems, and results in fixed time replacement of components. In comparison, a prognostic approach aims at predicting failure based on individual component state estimation by employing either physics of failure, data or fusion degradation models [32].…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

“…This can include degradation models or damage accumulation models based on failure modes mechanism, such as wear, fatigue, corrosion, and contamination [14]. On the other hand, the data-driven prognostics approach is an application of machine learning and statistical pattern recognition on data collected at system, subsystem or component level [32,36]. In practice, a prognostics architecture can rely on an individual method or a combination of the three.…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

et al. 2020

View full text Add to dashboard Cite

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion.

show abstract

A review of the role of prognostics in predicting the remaining useful life of assets

Cited by 10 publications

References 0 publications

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Contact Info

Product

Resources

About