Zhaoqiang Wang

An Additive Wiener Process-Based Prognostic Model for Hybrid Deteriorating Systems

Hybrid deteriorating systems, which are made up of both linear and nonlinear degradation parts, are often encountered in engineering practice, such as gyroscopes which are frequently utilized in ships, aircraft, and weapon systems. However, little reported literature can be found addressing the degradation modeling for a system of this type. This paper proposes a general degradation modeling framework for hybrid deteriorating systems by employing an additive Wiener process model that consists of a linear degradation part and a nonlinear part. Furthermore, we derive the analytical solution of the remaining useful life distribution approximately for the presented model. For a specific system in service, the posterior estimates of the stochastic parameters in the model are updated recursively by using the condition monitoring observations based on a Bayesian framework with the consideration that the stochastic parameters in the linear and nonlinear deteriorating parts are correlated. Thereafter, the posterior distribution of stochastic parameters is used to update in real-time the distribution of the remaining useful life where the uncertainties in the estimated stochastic parameters are incorporated. Finally, a numerical example and a practical case study are provided to verify the effectiveness of the proposed method. Compared with two existing methods in literature, our proposed degradation modeling method increases the one-step prediction accuracy slightly in terms of mean squared error, but gains significant improvements in the estimated remaining useful life.

A prognostics-based spare part ordering and system replacement policy for a deteriorating system subjected to a random lead time

Prognostics-based spare part ordering and system replacement (PSOSR) policies are at the forefront of the prevalent prognostics and health management discipline. However, almost all of the existing researches in this domain ignore the stochasticity of the lead time. With this in mind, this paper proposes a PSOSR policy based on the real-time health condition of a deteriorating system subjected to a random lead time. In doing so, the degradation path of the interested system is modelled by a Wiener process, and the associated life distributions can be predicted recursively according to the real-time health condition of the system. In turn, the proposed policy can also be updated dynamically based on these real-time obtained life distributions. The policy, which – in addition to incorporating the stochasticity of the lead time – integrates the decision-making issues of both spare part ordering and system replacement – is finally applied to a case study of an inertial navigation system served in a type of aircraft. The experimental results validate the policy’s effectiveness and superiority.

A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring

Some systems may spend most of their time in storage, but once needed, must be fully functional. Slow degradation occurs when the system is in storage, so to ensure the functionality of these systems, condition monitoring is usually conducted periodically to check the condition of the system. However, taking the condition monitoring data may require putting the system under real testing situation which may accelerate the degradation, and therefore, shorten the storage life of the system. This paper presents a case study of condition-based remaining storage life prediction for gyros in the inertial navigation system on the basis of the condition monitoring data and the influence of the condition monitoring data taking process. A stochastic-filtering-based degradation model is developed to incorporate both into the prediction of the remaining storage life distribution. This makes the predicted remaining storage life depend on not only the condition monitoring data but also the testing process of taking the condition monitoring data, which the existing prognostic techniques and algorithms did not consider. The presented model is fitted to the real condition monitoring data of gyros testing using the maximum likelihood estimation method for parameter estimation. Comparisons are made with the model without considering the process of taking the condition monitoring data, and the results clearly demonstrate the superiority of the newly proposed model.

A Prognostic-Information-Based Order-Replacement Policy for a Non-Repairable Critical System in Service

This paper proposes a prognostic-information-based joint order-replacement policy for a non-repairable critical system in service. The primary difference from existing work is to take the online condition monitoring data into consideration during the joint decision-making process. Towards this end, the systems degradation trajectory is modeled by a Wiener process whose parameters are real-time estimated based on the newly obtained condition monitoring data by utilizing the expectation maximization algorithm and Bayesian inference. By doing so, the remaining useful life distribution of the system of interest can be predicted in real-time, which is then used as the prognostic information to dynamically update the optimal ordering and replacement times jointly. This process makes the jointly obtained order-replacement decisions rely on the prognostic information available from the systems degradation monitoring. Finally, a practical case study of the inertial navigation system in aircraft is provided to validate the proposed joint decision policy.

A Simulation-based Remaining Useful Life Prediction Method Considering the Influence of Maintenance Activities

As the key of the prevalent prognostics and health management, remaining useful life prediction has attracted considerable attentions during the past decades. However, almost all of the existing remaining useful life prediction methods were implemented under the premise that the deteriorating systems were not maintained over the whole life cycle. For the deteriorating systems experiencing maintenance activities during their life profiles, this paper presents a simulation-based remaining useful life prediction method taking the influence of maintenance activities into account. Specifically, the Wiener process with jumps is employed to model the degradation path of a deteriorating system, where the jump parts are used to characterize the influence of maintenance activities on the system degradation. The parameters in the degradation model are estimated by the maximum likelihood estimation method. To acquire the remaining useful life distributions of the deteriorating system, we design a simulation-based algorithm on the basis of the Markov Chain Monte Carlo method. Accordingly, the interested statistics associated with the remaining useful life can be obtained numerically. Finally, a numerical example is provided to show the implementation of the newly proposed remaining useful life prediction method.

An off-online fuzzy modelling method for fault prognosis with an application

Fault prognosis plays a key role in prognostics and health management (PHM). Currently, there are many methods to predict the occurrence of a fault, but the fuzzy model has become an effective alternative owing to its advantage of using not only quantitative data but also qualitative information with fuzzy uncertainty. It is particularly useful for a dynamic system with complexity, morbidity and nonlinearity. Compared with the prediction from an offline fuzzy model, an online prediction is more desired, since we can monitor the health condition of a system and achieve fault prognosis in real time. In this paper, we develop a prediction model by firstly establishing an initial fuzzy model with offline information, then, using the online information to adjust the initial offline model in real time. The offline fuzzy model is modelled through a relevance vector machine (RVM) method and the structure of the initial fuzzy model is adjusted based on the statistical utility of a fuzzy rule using online information. The model parameters are optimized by the gradient decent (GD) algorithm. To do so, a fault prognosis algorithm is proposed on the basis of our off-online fuzzy modelling method. Finally, the proposed fuzzy modelling method and its fault prognosis algorithm are applied to a practical example. The empirical results show that our developed method has a significant improvement over the existing fuzzy modelling methods in terms of accuracy and the corresponding fault prognosis algorithm is effective.

A novel life prediction method for equipment considering the influence of imperfect maintenance activities

As the key component of prognostic and health management (PHM), life prediction for equipment plays a more and more significant role during the past decades. In current literature, there have been a few studies highlighting life prediction for equipment subjected to the influence of imperfect maintenance activities. Aiming at this problem, we firstly put forward a stochastic degradation model based on the Wiener process considering the influence of imperfect maintenance activities on the degradation level and degradation rate. Then, the theoretical expression of life probability distribution can be derived under the concept of first hitting time using the convolution operation, and the approximate expression of life probability distribution can be derived by the Monte Carlo simulation algorithm. A numerical example is provided to demonstrate the practicality and effectiveness of the newly proposed life prediction method.

A new remaining useful life prediction approach for independent component based on the Wiener process and Bayesian estimating paradigm

Online remaining useful life (RUL) prediction is the key of prognostics and health management. Aiming at the problem that the online RUL prediction methods in existing literature usually rely on the history information of the other specimens of the same kind, a new online RUL prediction approach for independent component is proposed in this paper. The offline information for a certain independent component is collected and utilized to confirm the estimates of the parameters by maximum likelihood estimation (MLE) method, and then the obtained estimates are updated employing the Bayesian mechanism with the real time condition monitoring data. The RUL is defined on the concept of first hitting time; furthermore, the exact analytical solution for RUL distribution is deduced. For the validation of our proposed RUL prediction approach, a numerical example is provided. The results reflect that our approach gains a higher RUL prediction accuracy than some other online RUL predicting method in the existing literature.