9,413 research outputs found
Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox
The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and component capability define useful life, is key for services
profitability. Unfortunately, building prognosis models for large fleets is a
daunting task as factors such as duty cycle variation, harsh environments,
inadequate maintenance, and problems with mass production can lead to large
discrepancies between designed and observed useful lives. This paper introduces
a novel physics-informed neural network approach to prognosis by extending
recurrent neural networks to cumulative damage models. We propose a new
recurrent neural network cell designed to merge physics-informed and
data-driven layers. With that, engineers and scientists have the chance to use
physics-informed layers to model parts that are well understood (e.g., fatigue
crack growth) and use data-driven layers to model parts that are poorly
characterized (e.g., internal loads). A simple numerical experiment is used to
present the main features of the proposed physics-informed recurrent neural
network for damage accumulation. The test problem consist of predicting fatigue
crack length for a synthetic fleet of airplanes subject to different mission
mixes. The model is trained using full observation inputs (far-field loads) and
very limited observation of outputs (crack length at inspection for only a
portion of the fleet). The results demonstrate that our proposed hybrid
physics-informed recurrent neural network is able to accurately model fatigue
crack growth even when the observed distribution of crack length does not match
with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer
perceptron, the stress intensity and Paris law layers, the cumulative damage
cell, as well as python driver scripts) used in this manuscript are publicly
available on GitHub at https://github.com/PML-UCF/pinn. The data and code are
released under the MIT Licens
Review of Health Prognostics and Condition Monitoring of Electronic Components
To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted
Condition-based maintenance—an extensive literature review
This paper presents an extensive literature review on the field of condition-based
maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric
indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality
reduction, concerning the metric of the co-citations of the papers. Four main research areas have been
identified, able to delineate the research field synthetically, from theoretical foundations of CBM;
(i) towards more specific implementation strategies (ii) and then specifically focusing on operational
aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric
results have been combined with an interpretative research to extract both core and detailed concepts
related to CBM. This combined analysis allows a critical reflection on the field and the extraction of
potential future research directions
Multidimensional prognostics for rotating machinery: A review
open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment
Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review
With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring
Vehicle level health assessment through integrated operational scalable prognostic reasoners
Today’s aircraft are very complex in design and need constant monitoring of the
systems to establish the overall health status. Integrated Vehicle Health
Management (IVHM) is a major component in a new future asset management
paradigm where a conscious effort is made to shift asset maintenance from a
scheduled based approach to a more proactive and predictive approach. Its goal is
to maximize asset operational availability while minimising downtime and the
logistics footprint through monitoring deterioration of component conditions.
IVHM involves data processing which comprehensively consists of capturing data
related to assets, monitoring parameters, assessing current or future health
conditions through prognostics and diagnostics engine and providing
recommended maintenance actions.
The data driven prognostics methods usually use a large amount of data to learn
the degradation pattern (nominal model) and predict the future health. Usually
the data which is run-to-failure used are accelerated data produced in lab
environments, which is hardly the case in real life. Therefore, the nominal model
is far from the present condition of the vehicle, hence the predictions will not be
very accurate. The prediction model will try to follow the nominal models which
mean more errors in the prediction, this is a major drawback of the data driven
techniques.
This research primarily presents the two novel techniques of adaptive data driven
prognostics to capture the vehicle operational scalability degradation. Secondary
the degradation information has been used as a Health index and in the Vehicle
Level Reasoning System (VLRS). Novel VLRS are also presented in this research
study. The research described here proposes a condition adaptive prognostics
reasoning along with VLRS
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