Remaining useful life (RUL) prediction of component/system has drawn significant attention from researchers and practitioners because of its great importance for improving reliability and availability of engineering systems. In the context of condition based maintenance/prognostics and health management (CBM/PHM) [
1,
2], accurate online prediction on RUL of component/system turns out to be the key for the success of this advanced maintenance technology in system health management. Prognostic algorithms have been proposed in a large number of publications for various industrial applications. Those algorithms are mostly either data-driven [
3‐
7], physically motivated [
8‐
16] or model and data integrated [
17‐
26].
In this paper, an integrated prognosis method is developed to account for both the uncertainty in crack initiation time (CIT) and the shock in the degradation. The introduction of uncertainty in CIT is the key to deal with the discontinuity caused by shock in the degradation path. A crack is typically initiated in a component, propagates due to stress, and eventually will cause component failure. We define CIT as the time instant when the damage is detected and the prognosis starts. Indeed, when the CIT is adjusted, it is the “intercept” with the time axis of degradation path at the initial crack size that is adjusted, which is a newway of adjustment. The combination of adjustment in both “slope” and “intercept” will better characterize the real degradation path given the crack observations. To deal with the discontinuity in the shock degradation, we aim to find a virtual degradation path with an earlier CIT that could achieve the same failure time as the shock degradation.
1.1 Literature Review
Prognostic models are typically categorized into three groups: data-driven methods, physics-based methods and integrated methods.
Data-driven models are built purely on data such as sensor-collected condition monitoring data. They typically require large amount of data to achieve reasonable accuracy. In Ref. [
3], a proportional-hazards model with time dependent stochastic covariates was used as lifetime model to predict the failure rate and to optimize the unit replacement policy. The set of failure time and covariates was assumed to be a joint nonhomogeneous Markov process. The maximum likelihood was applied to estimate the model parameters including the coefficients of covariates and the transition probabilities of the Markov process. In Ref. [
4], the degradation model was selected to be exponential. Two error terms were considered: one was treated as a multiplicative random variable, and the other was treated as a multiplicative Brownian motion process. A Bayesian procedure was applied to update the parameters in the exponential model. As a research mainstream of data-driven models, various machine learning techniques were investigated in the literature. The authors of Ref. [
5] developed neural networks to predict bearing failure time, which aimed to train a relationship between the bearing service time and the corresponding vibration spectrum. The authors of Ref. [
6] developed a neural network to predict RUL using both failure and suspension condition monitoring histories. An extended recurrent neural network was proposed to predict the health condition of gears in Ref. [
7]. The incorporation of Elman context layer in the proposed networks enhanced its ability to model nonlinear time series. Data-driven models are straightforward to establish given sufficient and well-distributed data. Therefore, data of good quality availability is a prerequisite for data-driven models, which is rare for costly industrial equipment.
In contrast, physics-based models resort to physical laws governing the defect growth, where the values or distributions of the model parameters in the physical models, e.g., material dependent parameters, are kept constant and will not be updated based on condition monitoring information. A commonly used law to describe crack propagation is Paris’ law, which was originated in Ref. [
16]. Many publications are devoted to devising numerical algorithms to calculate the quantity (e.g., stress intensity factor) needed in Paris’ law. Readers can refer to Refs. [
8‐
10] for these approaches in which finite element modeling was discussed for stress analysis near crack tip. In the physics-based prognostic models, component failure time is defined as when the defect size exceeds a critical value. Authors of Ref. [
11] investigated several factors that influence the crack growth trajectory in the gear tooth, including backup ratio, initial crack location, fillet geometry, rim/web compliance, gear size and pressure angle. The research work in Ref. [
12] took account of the variation of moving load on gear tooth into crack growth prediction by breaking the tooth engagement into multiple steps. Authors in Ref. [
13] applied Paris’ law to predict fatigue crack growth with utilization of transmission error to estimate the current crack size, which improved predictive accuracy. The service life of gears was divided into crack initiation and crack propagation periods in Ref. [
14]. The strain-life method was used to determine the time required for crack initiation, while Paris’ law was used to obtain the time required for the crack to grow from initial crack size to the critical value. Kacprzynksi et al. [
15] developed a prognostic tool which predicts gear failure probability by fusing physics-of-failure models and diagnostics information. The results showed variance reduction in failure probability when diagnostics information was present. Physical models can achieve high predictive accuracy if appropriately built. However, it demands comprehensive physics theory and intensive computation to build physical models of high-fidelity. Furthermore, physical models are unavailable for complex systems which limit its usages in real-world applications. In addition, physical models are deterministically used in the above mentioned literature, which means they are unable to address the uncertainty in failure.
Recently, integrated prognostics methods [
17‐
26] were developed to achieve real-time RUL prediction during the system operations by combining both condition monitoring (CM) data and physics of failure. The integrated methods usually have an updating process by assimilating observations, during which the uncertainty is expected to shrink so that the confidence increases in the predicted results. The integrated prognostics methods are more advantageous than physics-based methods in that the model parameters are able to be adjusted for a specific component in a specific working condition, and more advantageous than data driven methods in that massive data trending is not necessary. Bayesian framework allows for uncertainty quantification; hence, it is widely used in integrated prognostics methods. A problem of crack propagation in a fuselage panel of aircraft was considered in Ref. [
17]. Bayesian inference was used to characterize parameters in Paris’ law and error term. The work in Ref. [
18] extended the methods in Ref. [
17] to consider the correlation between model parameters. In authors’ prior work [
19], integrated prognostics methods were proposed for gear health prediction and uncertainty quantification. Physical models include Paris’ law, fracture mechanics model and one-stage gearbox dynamics model. Through Bayesian updating, the uncertainty in RUL prediction was reduced as crack measurements became available. To increase the efficiency of uncertainty quantification, the authors proposed to use polynomial chaos expansion to accelerate the Bayesian process in prognostics [
20]. Furthermore, an approach was devised to deal with time-varying operating conditions to make them applicable in various loading environment [
21]. Bayesian framework was also used for bearing health prognostics. The bearing spall propagation was investigated in Ref. [
22] where Bayesian inference was applied to reduce prediction uncertainty. In recent years an increasing volume of literature was published that treated prognostic model as a dynamic system mainly because of its natural interface with real-time data. In Ref. [
23], a health indicator extracted from vibration signature was fed into Paris’ law to update the RUL of bearings. The update was implemented using a Kalman filter. Prognostic models in Refs. [
24‐
26] are established in a particle filtering framework, in which the problem of non-linear state transition and non-Gaussian noise can be tackled.
1.2 Motivations to Consider the Uncertainty in CIT
A large grain of inherent uncertainty imposes major challenges in prognostic methods development. There are various sources for uncertainties, such as micro-structure of material, operating conditions, working environment, measurement as well as human factors. Many research efforts go to addressing how to identify, capture and manage these multiple uncertainties to make the RUL prediction more accurate, precise and reliable. The existing prognostic approaches usually start the prediction at an assumed time instant when a fault at certain severity is detected. This is based on an assumption that the starting point of prognosis is accurate. However, due to the limitations of the fault detection and diagnostic technologies, there is a large variation in the accuracy of fault detection. This variation affects the prediction accuracy accordingly: an early starting point of prognostics will lead to underestimated RUL, and late starting point will lead to overestimated RUL. In authors’ prior work [
19‐
21], it was proposed to update the uncertain model parameters in Paris’ law to make RUL prediction more accurate by feeding CM data to a Bayesian framework. These model parameters actually determine the “slope” of degradation path in a scale of damage size versus time. What the Bayesian updating process does is adjusting the “slope” of the degradation path to maximize the likelihood of crack observations when an uninformative prior is given. Apart from the “slope”, another factor that controls the degradation path is the “intercept” with time axis. Hence, to better characterize the degradation path, it is needed to explicitly consider the uncertainty in CIT, which determines its “intercept” with time axis.
1.4 Shock Degradation Prediction Using CIT Adjustment
In the crack propagation problem, model parameters in the Paris’ law include material dependent coefficients, which should not be affected by external forcing factors, i.e., overload causing shock. Hence, additional uncertainty source other than model parameters is needed to account for the effect of shock on the degradation prediction. Because sudden damage increment results in a discontinuity in the degradation path, the lifetime is shortened accordingly. Note that, if the slope of degradation path is given as fixed, the degradation path with a shortened lifetime can be considered as equivalent to a gradual degradation path with an earlier CIT. Therefore, the variation in CIT provides a degree of freedom in translational adjustment for the degradation path. With both slope and translational adjustments, the proposed method is expected to reduce the uncertainty in RUL prediction and to capture the effect of shock on the degradation as well. In different applications, the shock occurrence time might be known or unknown. If known, the corrective action can be taken right after the shock occurs; otherwise additional work is needed. In this study, both the two situations will be considered.
The remainder of this paper is organized as follows. Section
2 introduces the integrated prognostics framework that gives a global view of the structure of the proposed method. In Section
3, the Bayesian updating procedure is presented to deal with gradual degradation considering uncertainty in both CIT and material parameters. Section
4 investigates integrated prognostics for degradation with shock. Two cases are considered: a). shock degradation with known shock occurrence time; b) shock degradation with unknown shock occurrence time. The formula to compute RUL is also given in this section. In Section
4, examples are given to show the effectiveness and efficacy of the methods. Section
5 concludes the work.