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Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies. Note to Practitioners—Degradation signals have been widely used in determining the current health condition and estimate the remaining useful life (RUL) of a component or a system. Most of the existing prognostics utilize a parametric regression model to describe the evolution path of degradation signals for RUL prediction. The common functional forms of these models include simple linear, quadratic, and exponential functions. However, in many applications, the degradation signals show multiple-segment characteristics and the existing parametric forms are inadequate to capture the degradation trend. Motivated by such issue, this paper presents a multiple-change-point (CP) modeling approach, where the degradation signal is divided into several consecutive segments by CPs, and each segment is modeled by a unique parametric model. To capture the heterogeneity across different units, all the parameters, including the number and locations of CPs and model parameters of each segment, are assumed to be random variables following certain distributions. Then, we develop a statistical method to estimate these distributions using historical data. At the online monitoring stage, we develop an innovative updating algorithm to exactly calculate the closed forms of the posterior distributions of the latest CP, the current segment, and model parameters of the current segment. We also derive a closed form for the RUL distribution estimation. Later, several efficient approximation strategies are proposed to reduce the computational burden. Simulation studies and real case studies have shown that the proposed methodology has much better performance than those of existing approaches in handling degradation signals of multiple-segment characteristics. [ABSTRACT FROM AUTHOR]

This paper is devoted to modeling, analysis, and improvement of flexible manufacturing systems with asynchronous exponential machines and finite nondedicated buffers. In such systems, each machine may process multiple types of products with different speeds, and buffers are shared for all products. Efficient algorithms to evaluate the system performance are developed. Formulas are derived to calculate line throughput in one- and two-machine lines, and a convergent recursive algorithm is introduced for longer lines. The numerical results show that the method leads to a high accuracy in performance evaluation. Using such a model, bottleneck analysis has been carried out to identify the machine or product whose improvement will lead to the largest improvement in system throughput. Indicators based on the collected data to identify bottleneck machine and product are derived without complete calculation of the partial derivatives of system performance. Such efficient algorithms provide a quantitative tool for analysis and improvement of flexible manufacturing systems. [ABSTRACT FROM AUTHOR]

This paper considers a fleet of identical units where each unit consists of the same critical components. The degradation state of each critical component is assumed to be monitored by an on-board sensor. The paper presents a methodology for identifying the top-k (the k most reliable) units in a fleet using sensor-based prognostic information. Specifically, we develop a Prognostics-Based Ranking (PBR) algorithm that combines stochastic degradation models with computer science database ranking algorithms. The stochastic degradation modeling framework is used to compute and update, in real-time, residual life distributions (RLDs) of the critical components of each unit. Using a base case exponential degradation model, we identify conditions necessary to establish stochastic ordering among the RLDs of similar components. A preference relationship, consistent with the stochastic ordering results, is then used to sort the units of the fleet based on the RLDs of their respective components. A database ranking algorithm, known as the Threshold Algorithm (TA), is then used to identify the top-k units without necessarily computing all the RLDs. The paper concludes with an illustrative example. Note to Practitioners-The Prognostics-Based Ranking Algorithm is well suited for maintenance planning and scheduling in large scale fleet management applications. The PBR algorithm uses sensor signals from identical components on different units in a fleet, and proceeds to rank these units based on the residual lives of these components. The amplitude of the signal and the corresponding operational time are used to make inferences of the statistical distribution of the residual life of the component that is being monitored. The degradation modeling framework presented herein can be used to model various types of degradation signals that can be characterized using parametric functional forms. In this paper, we present a base case model that is suitable for engineering applications where the sensor signals follow an exponential path. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for robotic kinematic calibration based on a minimal product of exponentials (POE)-based model for the applications where only position measurements are required. Both joint zero-offset errors and initial frame twist error can be involved in this model. Analysis of the identifiability of these errors shows that at most six elements of these parameters can be identified. It also suggests that at least three noncollinear points on the end-effector should be measured to maximize the identifiability. Compared with the traditional POE-based model with full pose (position and orientation) measurements, the minimal model with only position measurements outperforms in terms of convenience, efficiency, and accuracy. [ABSTRACT FROM PUBLISHER]

This research proposes a centralized method for planning and monitoring the motion of one or a few mobile robots in an environment where regions of interest appear and disappear based on exponential probability density functions. The motion task is given as a linear temporal logic formula over the set of regions of interest. The solution determines robotic trajectories and updates them whenever necessary, such that the task is most likely to be satisfied with respect to probabilistic information on regions. The robots' movement capabilities are abstracted to finite state descriptions, and operations as product automata and graph searches are used in the provided solution. The approach builds up on temporal logic control strategies for static environments by incorporating probabilistic information and by designing an execution monitoring strategy that reacts to actual region observations yielded by robots. Several simulations are included, and a software implementation of the solution is available. The computational complexity of our approach increases exponentially when more robots are considered, and we mention a possible solution to reduce the computational complexity by fusing regions with identical observations. [ABSTRACT FROM AUTHOR]