Prognostics and health management(PHM) is a promising technology to reduce the overall life cycle cost and improve flight readiness in the civil and military aviation field. As the prognostic algorithms are applied into monitoring condition of aircraft structures, avionics, wiring, control actuators, power supplies, propulsion systems and etc, more attention has been focused on establishing a common standard to validate whether the PHM algorithms or systems can reach the design requirements and compare the efficiency of the diagnostics and prognostics algorithms. Currently, there’re many standards in the diagnostics and test regions, such as condition monitoring and diagnosis standard in ISO, the engine health management standards in SAE and so on. All these PHM related standards may help developing the new standard. First, this paper identifies the specification of PHM standards from different applications. And the state-of-the-art in PHM related standards is also given. Second, the current PHM-related standards in various standardizations and agencies are overviewed and analyzed according to the CBM, PHM, IVHM and HUMS classification; Lastly, some suggestions are given for PHM standardization development, and the focus of the future work and directions are also proposed.

A Review of Current Prognostics and Health Management System Related Standards

As the need to improve reliability, availability and safety of technical systems increases, a large number of proactive maintenance strategies have been proposed. Of greater interest is the development of prognostic and health management strategies where maintenance is scheduled based on the current and the predicted future health state of a technical system. In addition, prognostic information can be used to control the reliability of intelligent mechatronic systems to ensure their mission objective is achieved. Therefore methodologies for estimating these current and future health states reliably and accurately are imperative. With the advancement in sensor technology, majority of the present day technical systems are installed with a network of sensors for condition or performance monitoring. This has led to the increased application of machine learning algorithms in condition monitoring. Depending on the sensor data available, different approaches for utilizing the data with machine learning algorithms can be applied. However, aguide for selecting the appropriate approach for a given system is either lacking or has not been explored extensively. Therefore, this work aims at providing a guide for selecting suitable approaches and machine learning algorithms for a given system depending on the available sensor data.Five approaches for prognostics and an ensemble of the best performing approaches are presented. Since the performance of machine learning algorithms is highly dependent on the input features, methods for feature extraction and selection are also presented. The approaches are evaluated and validated with run-to-failure condition monitoring data of actual systems. This information could serve as a guide for selecting the appropriate method for a given kind of system depending on the available condition monitoring data.

Development and Performance Evaluation of Prognostic Approaches for Technical Systems

This paper presents a Bayesian approach for maintenance action recommendation tested on the PHM 2013 Data Challenge dataset. The Challenge focused on maintenance action recommendation based on historical cases and the algorithms were evaluated on their ability to recommend confirmed problem types. The proposed approach is based on a Bayesian inference methodology and deals with recommending an already known problem type for each case. The recommender can be viewed as a classifier among the confirmed problem types. For each such problem type class the a priori probabilities for the events which characterize the problem type from the training data are estimated. When testing cases are presented, the recommender calculates the a posteriori probabilities for each of the confirmed problem types and suggests the type of problem that corresponds to the maximum a posteriori (MAP) probability.

A Bayesian Approach for Maintenance Action Recommendation

A rigorous methodology is presented for both specification and verification of prognostic algorithm performance. The prognostic algorithm specification statement takes the form, “The prognostic algorithm shall provide a minimum of hours time-to-maintenance such that between % and % of failures of component ABC will be avoided with % confidence.” The methodology is developed first for a single failure mode case and then extended to the multiple failure mode case. The case of non-prognosable failure modes is also considered. Finally, implications of this approach are presented, including pre- tabulation of confidence bounds, estimation of the minimum amount of data required to reach a given verification confidence, and a method for using a minimum confidence growth curve to account for initial low confidence in a prognostic algorithm.

/pdf/prognostic-algorithm-verification-4db46enpl6.pdf

Prognostic Algorithm Verification

The Prognostic and Health Management (PHM) technology has been widely used as means of improvement for reliability, safety and availability of new generation weapons and commercial products, achieving autonomic logistics. In avionics system, European countries and the USA have been promoting the development, maturing of PHM critical technologies and their applications in military and commercial aircrafts. PHM application and design ideas in avionics system are analyzed, functional relations of PHM system and architecture of health management system suitable for integrated avionics system are proposed. In order to improve the prognostic ability of the avionic data processing modules, some ageing test has been done, but it will take a long time to degenerate the components and modules to the critical point, varies fault injection ways are introduced in the paper. The novel way can calculate the life expectancy by combining the failure physics modules, reduce the ageing test workload, which is significant to life prediction. So, this PHM system can play a very important role in avionics data processing modules.

A PHM research for data processing modules in avionics system

Targeting on the verification and validation of prognostic and health management functional design, the performance assessment metrics and methods are rare. Aiming at establishing an application-oriented metrics system model, a set of testability, diagnosis, prognosis and efficiency algorithm performance metrics for the PHM system is described on the basis of performance requirements. The analytic hierarchy process is used to compare and decide PHM performance metrics system, and the weights are computed. The fuzzy comprehensive assessment is used to generalize the specified metrics. A comprehensive assessment method on performance metrics is proposed by combining the above two methods and the assessment of PHM system function is achieved.

https://ieeexplore.ieee.org/iel7/6490493/6493645/06493850.pdf

Performance Metrics Assessment Method on Aircraft Prognostics and Health Management

With the deeply insight into the research on prognostic and health management(PHM), very little attention has been focused on conducting validation and performance evaluation in the airborne PHM systems. According to the designing flowchart, three validation methods are proposed with the sample implementation. In this paper, a dynamic semi-physical validation framework for the flight control system has been proposed. It also draws a specific classification and directory for current metrics based on different aspects. Exploring how standards currently under development within the IEEE and other authorities can be used to support PHM applications and demonstrating their trends in future development.

Research on validation and performance assessment method of airborne PHM system

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Performance Metrics Assessment Method on Aircraft Prognostics and Health Management

Research on validation and performance assessment method of airborne PHM system