Condition-based maintenance for a degrading system has been attached great importance in reducing unexpected failures and enabling the safe operation of the system. In this paper, we consider a condition-based replacement problem with observed degradation signals for the determination of the optimal replacement time of the system. The observed degradation signals contain some health related information of the system so that the system’s health state may be more accurately estimated and the future degradation progression of the system can be predicted. Based on the predicted degradation state, the concerned replacement problem is formulated in the framework of the Markov decision process and then a new degradation-modeling framework based on maximum likelihood estimation is proposed to analyze the degradation signals and to predict the future degradation state of the system. To solve the proposed condition-based replacement decision problem, we analyze structural properties of the optimal replacement policy and an optimal monotonic control limit solution policy is developed for condition-based replacement. Finally, a case study is provided to illustrate the proposed optimal replacement method.

This paper is concerned with estimating remaining useful life (RUL) for a class of stochastic degrading systems with surviving degradation paths and uncertain measurements. The motivation comes from two engineering facts: the system’s degradation state is discretely monitored and the measured degradation signals are taken from a survival degradation path, i.e., its lifetime is greater than the latest observation time, and the underlying degradation state cannot be perfectly observed due to the noise, disturbance, nonideal instruments, etc. Thus, the measured degradation signals are uncertain, but related to the underlying degradation state. Toward this end, we first present a general stochastic degradation modeling approach to account for the abovementioned facts. Then, a non-Gaussian degradation state transition equation is derived considering the constraint of the survival path and thus the particle filtering algorithm is used to estimate the underlying degradation state in real time from uncertain measurements. Furthermore, we derive the RUL distribution based on the first-passage time concept which incorporates the uncertainty of the estimation for the degradation state and can be real-time updated based on the available surviving yet uncertain measurements. The novelty of this paper is to allow us to exclude the probability of failure between discrete monitoring times and to account for the impacts of surviving and uncertain measurements on estimating both the degradation state and the RUL. To apply the presented methodology, a maximum likelihood estimation framework is provided to determine the model parameters based on the expectation maximization algorithm together with particle filtering and smoothing methods. As a special case fallen into the presented framework, a linear degradation modeling and prognostic realization is discussed. Finally, we demonstrate the proposed approach by a case study.

Compared with the multisite radar, the monostatic radar has the disadvantages of a single viewing angle and less available information. In the presence of main-lobe jamming, the monostatic radar has the problems of poor anti-jamming effect and difficulty in achieving target detection. To solve the above problems, the radar signal processing method and data fusion method are combined in this paper. A main-lobe jamming suppression method for phased array (PA) netted radar based on the maximum signal-to-noise ratio blind source separation (MSNR-BSS) is proposed. Firstly, the received signals of a monostatic PA radar are separated into target signals and jamming signals by the MSNR-BSS method. Secondly, channel accumulation, pulse compression, and constant false-alarm rate (CFAR) target detection are performed on the separated signal channels to obtain the target radial distance. Finally, the data fusion theory of three global orientations is employed to determine the target spatial position. Simulation experiments evaluate the performance of main-lobe jamming suppression and verify the effectiveness of the proposed method. In the presence of noise amplitude modulation (NAM) jamming or smeared spectrum (SMSP) jamming (JNR=100dB), the target positioning accuracy of the proposed method is 6.613m or 6.596m. And the peak sidelobe level ratios (PSLR) in the two jamming environments are approximately equal to - 8dB.

In this study, a new intelligent fault diagnosis approach based on composite multi-scale dimensionless indicators (CMDIs) and affinity propagation (AP) clustering is proposed to identify working conditions of mechanical components. For this goal, CMDIs as fault features are for the first time proposed to search more informative fault information hidden in the original vibration signals and useful variational mode decomposition signals. Next, two feature evaluation methods, Fisher criterion (FC) and self-weight criterion (SW), are introduced to search the top ranked CMDIs, respectively. Subsequently, AP clustering is applied to select the important CMDIs. The most important CMDI of each cluster is chosen as the final fault feature. Finally, these important CMDIs are fed into the following supervised learning classifiers to identify working conditions of mechanical components: k-nearest neighbors, linear discriminant analysis, naive Bayesian and random forests. Two experimental examples for different fault types of mechanical components are conducted. Experimental comparison results demonstrate that the combination diagnosis model based on CMDIs-FC-AP with naive Bayesian classifier would be most suitable for complex samples with a high dimensional feature set.

As the fundamental and prerequisite work of remaining useful life (RUL) prediction, degradation modeling directly affects the accuracy of RUL prediction. Existing degradation models are all developed under a single time scale, and less research has been carried out to consider the impact of multiple time scales on the degradation model. Toward this end, we mainly study a nonlinear degradation modeling and RUL prediction method for nonlinear stochastic degraded equipment with bivariate time scales in this paper. Firstly, a nonlinear degradation model considering the influence of two time scales is constructed based on the diffusion process. At the same time, the relationship between the two scales is quantitatively characterized by random proportional coefficient. Then, the analytical expressions of the life and RUL for nonlinear degraded equipment are derived under the concept of first passage time (FPT). In order to realize the adaptive estimation of parameters, the model parameters estimation method based on maximum likelihood estimation (MLE) and Kalman filtering algorithm is developed in this paper. Finally, numerical simulation and the monitoring data of gyroscope verify the effectiveness and superiority of the proposed method. The experimental results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction, and has a broad engineering application space.