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

Abstract

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.

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

“…Table 2 shows a chromosome in DPGA, which is implemented by a one-dimensional list consisting of categorical and continuous variables to represent algorithm-selection and parameter-specification. Specifically, it is composed of four parts corresponding to data rebalancing, feature extraction, feature reduction, and learning subtasks as follows: In case of SVM: The penalty parameter C ∈ [1][2][3][4][5][6][7][8] In case of RF: The number of trees n tree ∈ [2-10]…”

“…Various approaches have been proposed in each problem and they can be divided into three categories: Physics-based, data-driven, and hybrid-based approaches. Physics-based approaches incorporate prior system-specific knowledge from an expert, as shown in previous studies, of fault-type classification [2][3][4][5] and RUL prediction [6][7][8][9] problems. Alternatively, data-driven approaches are based on statistical-/machine-learning techniques using the historical data (see example studies about aero-propulsion system simulation (C-MAPSS) dataset) problems.…”

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

“…Wang et al [12] investigated a hybrid prognosis approach for machine condition prognosis of bearings in a wind turbine. Cubillo et al [13] presented a potential physics based on the prediction model for rotating machinery to describe the degradation process of the machinery. Chadli et al [14] developed a novel method based on the design of distributed state estimation and fault detection and isolation filters, and its effectiveness was illustrated by a numerical example.…”

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

“…Measurements of the normal stage and the degradation stage are both simulated. A nonlinear degradation path is generated according to the running process of bearings with (10), (12), and (13), which incorporate four uncertainty sources. The degradation path includes the measurement time and the corresponding HI extracted from sensor data.…”

“…The model-based approaches can be generally divided into two categories, physics model-based methods and statistical model-based methods [11]. Physics model-based methods construct the models based on the failure mechanisms of equipment [12]. The Paris law is one of the most commonly used physics model in describing the degradation process of machinery, and many investigations employed it and its variants for RUL prediction [6,7,13].…”