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In today’s global competitive marketplace, there is intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. With the advent of Internet and tether-free technologies, companies necessitate dramatic changes in transforming traditional ‘‘fail and fix (FAF)’’ maintenance(More)
De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and(More)
Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic, resulting in costly downtime. One of the key issues in bearing prognostics is to detect the defect at its incipient stage and alert the operator before it develops into a catastrophic failure. Signal de-noising and extraction of the weak(More)
Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions for all operational scenarios. This paper summarizes a physics-based remaining useful life (RUL) prediction method developed in the DARPA Engine System Prognosis (ESP) program. This investigation focuses on a(More)
The project integrates work in natural language processing, machine learning, and the semantic web, bringing together these diverse disciplines in a novel way to address a real problem. The objective is to extract and categorize machine components and subsystems and their associated failures using a novel approach that combines text analysis, unsupervised(More)
A class of large-scale systems, which is naturally divided into many smaller interacting subsystems, is usually controlled by a distributed or decentralized control framework. In this case, how to improve the performance of the entire system is a problem. A novel distributed model predictive control (MPC) is proposed for improving the global performance.(More)
Gaussian process (GP), as one of the cornerstones of Bayesian non-parametric methods, has received extensive attention in machine learning. GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hieratical modeling and no requirement for a priori information of function forms in Bayesian reference. In light of its(More)
The real-time fault detection and diagnosis are critical for healthy operation of electromechanical systems, of which the complex characteristics affect the performance of current shop floor fault diagnosis methods. Aiming to overcome the drawbacks, this paper presents a new fault diagnosis method using a newly developed method, support vector machines(More)
Classification techniques have been widely used in fault prediction for industrial systems. However, an inherent issue with this approach is label imperfections in training data, since the line of demarcation between classes is determined based on field expert experience and maintenance capability. To address this issue we propose a noisy-label model in(More)