Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.

Current status of machine prognostics in condition-based maintenance: a review

Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

https://ti.arc.nasa.gov/m/profile/schwabac/PHM_Metrics_Final.pdf

Metrics for evaluating performance of prognostic techniques

A summary of fault modelling and predictive health monitoring of rolling element bearings

This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM) The method relies on two phases: an off-line phase, and an on-line phase During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence The developed method is tested on benchmark data taken from the “NASA prognostics data repository” related to several experiments of failures on bearings done under different operating conditions Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance

/pdf/a-data-driven-failure-prognostics-method-based-on-mixture-of-49ddd6mw8p.pdf

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

Watchdog agent - An infotronics-based prognostics approach for product performance degradation assessment and prediction

Prognostic algorithms for condition based maintenance of critical machine components are presenting major challenges to software designers and control engineers. Predicting time-to-failure accurately and reliably is absolutely essential if such maintenance practices are to find their way into the industrial floor. Moreover, means are required to assess the performance and effectiveness of these algorithms. This paper introduces a prognostic framework based upon concepts from dynamic wavelet neural networks and virtual sensors and demonstrates its feasibility via a bearing failure example. Statistical methods to assess the performance of prognostic routines are suggested that are intended to assist the user in comparing candidate algorithms. The prognostic and assessment methodology proposed here may be combined with diagnostic and maintenance scheduling methods and implemented on a conventional computing platform to serve the needs of industrial and other critical processes.

/pdf/fault-prognosis-using-dynamic-wavelet-neural-networks-qmqywf9gel.pdf

P. Wang

Papers

Fault prognosis using dynamic wavelet neural networks