Abstract
Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
Highlights
Reliability is defined as the ability of a product or system to perform its required functions without failure for a specified time, and when used under specified conditions
This paper proposes a deep generative state-space modeling methodology for the remaining useful life prognostics of physical assets
Expert black swan events,conditions, abnormalknowledge operating of the underlying failureof modes, physics of failure partially relevant information, can all conditions, knowledge the underlying failure models modes,and physics of failure models and partially be included within the remaining useful life estimation. While this information can be valuable, the relevant information, can all be included within the remaining useful life estimation
Summary
Reliability is defined as the ability of a product or system to perform its required functions without failure for a specified time, and when used under specified conditions. Reliability engineering has been given technologies incorporating cheap sensing with the Internet of Things (IoT) generating multi-dimensional data sets through Industry 4.0 [1]. With this new data at the engineer’s fingertips, more sophisticated methodologies to handle this data have been developed and expanded within the prognostics and health management (PHM) field. These data sets are often costly and time-consuming to label [2].
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