A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Prognostics and Health Management of Industrial Equipment

ISBN13: 9781466620957Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL)

A new hybrid prognostic methodology

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available

Uncertainty Quantification in Fatigue Crack Growth Prognosis

This paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. Crack growth under variable amplitude loading is modeled using a modified Paris law that includes retardation effects. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis. The effect of different types of uncertainty – physical variability, data uncertainty and modeling errors – on crack growth prediction is investigated. The various sources of uncertainty include, but not limited to, variability in loading conditions, material parameters, experimental data, model uncertainty, etc. Three different types of modeling errors – crack growth model error, discretization error and surrogate model error – are included in analysis. The different types of uncertainty are incorporated into the crack growth prediction methodology to predict the probability distribution of crack size as a function of number of load cycles. The proposed method is illustrated using an application problem, surface cracking in a cylindrical structure

Physics-based prognostic modelling of filter clogging phenomena

In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results

A Hybrid Approach of Data-driven and Physics-based Methods for Estimation and Prediction of Fatigue Crack Growth

Lamb-wave-based nondestructive testing and evaluation (NDT/E) methods have drawn much attention due to their potential to inspect plate-like structures in a variety of industrial applications. To estimate and/or predict fatigue crack growth, many research efforts have been made to develop data-driven or physics-based methods. Data-driven methods show high predictive capability without the need for physical domain knowledge; however, fewer data can lead to overfitting in the results. On the other hand, physics-based methods can provide reliable results without the need for measured data; however, small amounts of physical information can worsen their predictive capability. In real applications, both the measurable data and the physical information of systems may be considerably limited; it is thus challenging to estimate and/or predict the crack length using either the data-driven or physics-based method alone. To make use of the advantages and minimize the disadvantages of each method, the work outlined in this paper aims to develop a hybrid approach that combines the data-driven and the physics-based methods for estimation and prediction of fatigue crack growth with and without Lamb wave signals. First, with Lamb wave signals, a data-driven method based on signal processing and the random forest model can be used estimate crack lengths. Second, in the absence of Lamb wave signals, a physics-based method based on an ensemble prognostics approach and Walker’s equation can be used to predict crack lengths with the help of the previously estimated crack lengths. To demonstrate the validity of the proposed approach, a case study is presented using datasets provided in the 2019 PHM Conference Data Challenge by the PHM Society. The case study confirms that the proposed method shows high accuracy; the RMSEs for specimens T7 and T8 are calculated as 0.2021 and 0.551, respectively. A penalty score is calculated as 7.63; this result led to a 2nd place finish in the Data Challenge. To the best of the authors’ knowledge, this is the first attempt to propose a hybrid approach for estimation and prediction of fatigue crack growth

A hybrid prognostics approach to estimate the residual useful life of a planetary gearbox with a local defect

A hybrid prognostics approach for the monioring of a planetary gearbox with the local defect is presented. This hybrid method can predict the remaining useful life (RUL) of planetary gearbox with a fatigue crack. The method consists of a dynamical model for simulation data generation, a statistical algorithm for feature selection and weighting, and a modified grey model for RUL prediction. Experimental studies are conducted to validate and demonstrate the feasibility of the proposed method for RUL prediction of a cracked sun gear in planetary gearbox. And the validation has a promising result

Model-Form Uncertainty Quantification in Prognosis and Fleet Management with Physics-Informed Neural Networks

Prognosis and health management play an important role in the control of costs associated with the operation of large industrial equipment. By properly comprehending hardware degradation and accurately predicting the remaining useful life of such equipment, we can significantly lower operational costs by reducing asset downtime and maintenance interventions. However, complex interactions between operational conditions and component capability make accurately modeling damage accumulation for large fleets a daunting task. Unforeseen factors such as aggressive missions introduced by operators, exposure to harsh environments, manufacturing issues, amongst many others, can lead to large discrepancies between predicted and observed useful life. Motivated by the growing availability of data and computational power as well as the advances in hybrid modeling frameworks, capable of merging elements of physics, machine learning, and statistical learning, in this dissertation, we focus on the development of novel approaches to minimize the impact of unforeseen factors in fleet management. In this dissertation, we focus on the challenges of accounting for the impacts of such unforeseen factors on two specific stages of a component service life; early-file and end-life. Two numerical case studies are derived to emulate two common issues in fleet life management; manufacturing issues leading to an infant mortality problem, and unexpected exposure to harsher environments by operators, accelerating wear-out and significantly reducing component\u27s useful life. In the first analysis, two key aspects in a prognosis and health management perspective are addressed; detecting the emerging issue (i.e., the infant mortality problem), and the evaluation of risk mitigation procedures to minimize/mitigate its effects on the overall fleet reliability. Bayesian networks implementing physics-based models are used to model the fleet unreliability and assist in the quantification of the infant mortality impact on the fleet useful life. Additionally, steps to adapted the derived Bayesian networks to assist in the evaluation of possible mitigation approaches to minimize the impacts of fleet-wide early life problems are presented. Concerning the wear-out analysis, a civil aviation case study is derived, in which an aircraft fleet mainly operates in coastal routes, significantly increasing its exposure to saline corrosion. These conditions lead to accelerated degradation of the aircraft wing panels due to the combined effects of corrosion and mechanical fatigue. Such corrosive conditions are not accounted for by the fleet prognosis model generating a significant epistemic uncertainty (i.e., a missing physics issue). To address this issue, we proposed hybrid recurrent neural network modules to compensate for the model-form uncertainty. In the formulated neural network cell, well-understood aspects of the degradation mechanism are addressed by a physics-based model, while data-driven models are trained to account for the missing physics effects. After proper training, the hybrid neural network can compensate for the unaccounted effects in the model damage forecast and generates accurate predictions to assist in the fleet prognosis analysis. Obtained results illustrate the capabilities of the proposed frameworks in compensating for the considered unforeseen factors impacts in fleet management. Additionally, the obtained results have prominently shown the significance and importance of properly account for such factors on fleet prognosis and how these factors can drastically hinder engineers\u27 ability to properly perform prognosis and health management analysis