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

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

Damage Tolerant Active Contro l: Concept and State of the Art

Damage tolerant active control is a new research area relating to fault tolerant control design applied to mechanical structures. It encompasses several techniques already used to design controllers and to detect and to diagnose faults, as well to monitor structural integrity. Brief reviews of the common intersections of these areas are presented, with the purpose to clarify its relations and also to justify the new controller design paradigm. Some examples help to better understand the role of the new area

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

A BAYESIAN FRAMEWORK FOR STRUCTURAL HEALTH MANAGEMENT USING ACOUSTIC EMISSION MONITORING AND PERIODIC INSPECTIONS

Many aerospace and civil infrastructures currently in service are at or beyond their design service-life limit. The ability to assess and predict their state of damage is critical in ensuring the structural integrity of such aging structures. The empirical models used for crack growth prediction suffer from various uncertainties; these models are often based on idealized theories and simplistic assumptions and may fail to capture the underlying physics of the complex failure mechanisms. The other source of uncertainty is the scarcity of relevant material-level test data required to estimate the parameters of empirical models. To avoid in-service failure, the structures must be inspected routinely to ensure no damage of significant size is present in the structure. Currently, the structure has to be taken off line and partly disassembled to expose the critical areas for nondestructive inspection (NDI). This is an expensive and time-consuming process. Structural health monitoring (SHM) is an emerging research area for online assessment of structural integrity using appropriate NDI technology. SHM could have a major contribution to the structural diagnosis and prognosis. Empirical models, offline periodic inspections and online SHM systems can each provide an independent assessment of the structural integrity; in this research, a novel structural health management framework is proposed in which the Bayesian knowledge fusion technique is used to combine the information from all sources mentioned above in a systematic manner. This work focuses on monitoring fatigue crack growth in metallic structures using acoustic emission (AE) technology. Fatigue crack growth tests with real-time acoustic emissions monitoring are conducted on CT specimens made of 7075 aluminum. Proper filtration of the resulting AE signals reveals a log-linear relationship between fracture parameters (da/dN and ΔK ) and select AE features; a flexible statistical model is developed to describe the relationship between these parameters. Bayesian regression technique is used to estimate the model parameters using experimental data. The model is then used to calculate two important quantities that can be used for structural health management: (a) an AE-based instantaneous damage severity index, and (b) an AE-based estimate of the crack size distribution at a given point in time, assuming a known initial crack size distribution. Finally, recursive Bayesian estimation is used for online integration of the structural health assessment information obtained from various sources mentioned above. The evidence used in Bayesian updating can be observed crack sizes and/or crack growth rate observations. The outcome of this approach is updated crack size distribution as well as updated model parameters. The model with updated parameters is then used for prognosis given an assumed future usage profile

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

Modeling generic size features of delamination, such as area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it holds important information for damage diagnosis and prognosis, including the delamination area, its center, and perimeter, useful for structural safety evaluation. In this context, this paper develops a novel particle filter (PF)-based framework for delamination shape prediction. To this end, the delamination image is discretized by a mesh, where control points are defined as intersections between the grid lines and the perimeter of the delamination. A parametric data-driven function maps each point position as a function of the load cycles and is initially fitted on a sample test. Then, a PF is independently implemented for each node whereby to predict their future positions along the mesh lines, thus allowing delamination shape progression estimates. The new framework is demonstrated with reference to experimental tests of fatigue delamination growth in composite panels with ultrasonics C-scan monitoring