159 research outputs found
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
Failure Prognosis of Wind Turbine Components
Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
A core part of maintenance planning is a monitoring system that provides a
good prognosis on health and degradation, often expressed as remaining useful
life (RUL). Most of the current data-driven approaches for RUL prediction focus
on single-point prediction. These point prediction approaches do not include
the probabilistic nature of the failure. The few probabilistic approaches to
date either include the aleatoric uncertainty (which originates from the
system), or the epistemic uncertainty (which originates from the model
parameters), or both simultaneously as a total uncertainty. Here, we propose
ensemble neural networks for probabilistic RUL predictions which considers both
uncertainties and decouples these two uncertainties. These decoupled
uncertainties are vital in knowing and interpreting the confidence of the
predictions. This method is tested on NASA's turbofan jet engine CMAPSS
data-set. Our results show how these uncertainties can be modeled and how to
disentangle the contribution of aleatoric and epistemic uncertainty.
Additionally, our approach is evaluated on different metrics and compared
against the current state-of-the-art methods.Comment: 6 pages, 2 figures, 2 tables, conference proceedin
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
On top of machine learning models, uncertainty quantification (UQ) functions
as an essential layer of safety assurance that could lead to more principled
decision making by enabling sound risk assessment and management. The safety
and reliability improvement of ML models empowered by UQ has the potential to
significantly facilitate the broad adoption of ML solutions in high-stakes
decision settings, such as healthcare, manufacturing, and aviation, to name a
few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods
for ML models with a particular focus on neural networks and the applications
of these UQ methods in tackling engineering design as well as prognostics and
health management problems. Toward this goal, we start with a comprehensive
classification of uncertainty types, sources, and causes pertaining to UQ of ML
models. Next, we provide a tutorial-style description of several
state-of-the-art UQ methods: Gaussian process regression, Bayesian neural
network, neural network ensemble, and deterministic UQ methods focusing on
spectral-normalized neural Gaussian process. Established upon the mathematical
formulations, we subsequently examine the soundness of these UQ methods
quantitatively and qualitatively (by a toy regression example) to examine their
strengths and shortcomings from different dimensions. Then, we review
quantitative metrics commonly used to assess the quality of predictive
uncertainty in classification and regression problems. Afterward, we discuss
the increasingly important role of UQ of ML models in solving challenging
problems in engineering design and health prognostics. Two case studies with
source codes available on GitHub are used to demonstrate these UQ methods and
compare their performance in the life prediction of lithium-ion batteries at
the early stage and the remaining useful life prediction of turbofan engines
Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data.
A considerable number of engineering assets are fast reaching and operating beyond their
orignal design lives. This is the case across various industrial sectors, including oil and
gas, wind energy, nuclear energy, etc. Another interesting evolution is the on-going
advancement in cyber-physical systems (CPS), where assets within an industrial plant are
now interconnected. Consequently, conventional ways of progressing engineering assets
beyond their original design lives would need to change. This is the fundamental research
gap that this PhD sets out to address. Due to the complexity of CPS assets, modelling
their failure cannot be simplistically or analytically achieved as was the case with older
assets. This research is a completely novel attempt at using advanced analytics techniques
to address the core aspects of asset life extension (LE). The obvious challenge in a system
with several pieces of disparate equipment under condition monitoring is how to identify
those that need attention and prioritise them. To address this gap, a technique which
combined machine learning algorithms and practices from reliability-centered
maintenance was developed, along with the use of a novel health condition index called
the potential failure interval factor (PFIF). The PFIF was shown to be a good indicator of
asset health states, thus enabling the categorisation of equipment as “healthy”, “good ” or
“soon-to-fail”. LE strategies were then devoted to the vulnerable group labelled “good –
monitor” and “soon-to-fail”. Furthermore, a class of artificial intelligence (AI) algorithms
known as Bayesian Neural Networks (BNNs) were used in predicting the remaining
useful life (RUL) for the vulnerable assets. The novelty in this was the implicit modelling
of the aleatoric and epistemic uncertainties in the RUL prediction, thus yielding
interpretable predictions that were useful for LE decision-making. An advanced analytics
approach to LE decision-making was then proposed, with the novelty of implementing
LE as an on-going series of activities, similar to operation and maintenance (O&M). LE
strategies would therefore be implemented at the system, sub-system or component level,
meshing seamlessly with O&M, albeit with the clear goal of extending the useful life of
the overall asset. The research findings buttress the need for a paradigm shift, from
conventional ways of implementing LE in the form of a project at the end of design life,
to a more systematic approach based on advanced analytics.Shafiee, Mahmood (Associate)PhD in Energy and Powe
Computational framework for real-time diagnostics and prognostics of aircraft actuation systems
Prognostics and Health Management (PHM) are emerging approaches to product
life cycle that will maintain system safety and improve reliability, while
reducing operating and maintenance costs. This is particularly relevant for
aerospace systems, where high levels of integrity and high performances are
required at the same time. We propose a novel strategy for the nearly real-time
Fault Detection and Identification (FDI) of a dynamical assembly, and for the
estimation of Remaining Useful Life (RUL) of the system. The availability of a
timely estimate of the health status of the system will allow for an informed
adaptive planning of maintenance and a dynamical reconfiguration of the mission
profile, reducing operating costs and improving reliability. This work
addresses the three phases of the prognostic flow - namely (1) signal
acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful
Life estimation - and introduces a computationally efficient procedure suitable
for real-time, on-board execution. To achieve this goal, we propose to combine
information from physical models of different fidelity with machine learning
techniques to obtain efficient representations (surrogate models) suitable for
nearly real-time applications. Additionally, we propose an importance sampling
strategy and a novel approach to model damage propagation for dynamical
systems. The methodology is assessed for the FDI and RUL estimation of an
aircraft electromechanical actuator (EMA) for secondary flight controls. The
results show that the proposed method allows for a high precision in the
evaluation of the system RUL, while outperforming common model-based techniques
in terms of computational time.Comment: 57 page
Degradation Vector Fields with Uncertainty Considerations
The focus of this work is on capturing uncertainty in remaining useful life (RUL) estimates for machinery and constructing some latent dynamics that aid in interpreting those results. This is primarily achieved through sequential deep generative models known as Dynamical Variational Autoencoders (DVAEs). These allow for the construction of latent dynamics related to the RUL estimates while being a probabilistic model that can quantify the uncertainties of the estimates
Multidimensional prognostics for rotating machinery: A review
Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and availability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidimensional 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 drawbacks 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
Advanced Sensing, Fault Diagnostics, and Structural Health Management
Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes
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