Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

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 focuses on predicting the future performance of a system, specifically the time at which the system no long performs its desired functionality, its time to failure. As an important aspect of prognostics, remaining useful life (RUL) prediction estimates the remaining usable life of a system, which is essential for maintenance decision making and contingency mitigation. A significant amount of research has been reported in the literature to develop prognostics models that are able to predict a system's RUL. These models can be broadly categorized into experience-based models, date-driven models, and physics-based models. However, due to system complexity, data availability, and application constraints, there is no universally accepted best model to estimate RUL. The review part of this paper specifically focused on the development of hybrid prognostics approaches, attempting to leverage the advantages of combining the prognostics models in the aforementioned different categories for RUL prediction. The hybrid approaches reported in the literature were systematically classified by the combination and interfaces of various types of prognostics models. In the case study part, a hybrid prognostics method was proposed and applied to a battery degradation case to show the potential benefit of the hybrid prognostics approach.

Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.

A Survey of Artificial Intelligence for Prognostics.

https://hal.archives-ouvertes.fr/hal-01050920/document

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

Impact technologies have developed a robust modeling paradigm for actuator fault detection and failure prediction. This model-based approach to prognostics and health management (PHM) applies physical modeling and advanced parametric identification techniques, along with fault detection and failure prediction algorithms, in order to predict the time-to-failure for each of the critical, competitive failure modes within the system. Advanced probabilistic fusion strategies are also leveraged to combine both collaborative and competitive sources of evidence, thus producing more reliable health state information. These algorithms operate only on flight control command/response data. This approach for condition-based maintenance provides reliable early detection of developing faults. As an advantage over 'black-box' health-monitoring schemes, faults and failure modes are traced back to physically meaningful system parameters, providing the maintainer with invaluable diagnostic and prognostic information. The developed model-based reasoner was validated and demonstrated on an electromechanical actuator (EMA) provided by Moog, Inc.

A model-based approach to prognostics and health management for flight control actuators

Actuators are complex electro-hydraulic or mechanical mechanisms utilized in aircraft to drive flight control surfaces, landing gear, cargo doors, and weapon systems. Impact has developed a prognostic and health management (PHM) methodology for these critical systems that includes signal processing and neural network tracking techniques, along with automated reasoning, classification, knowledge fusion, and probabilistic failure mode progression algorithms. The processing utilizes the command/response signal and hydraulic pressure data from the actuators and provides a real-time assessment of the current/future actuator health state. This methodology was applied to F/A-18 stabilator electro-hydraulic servo valves (EHSVs) using test stand data provided by Boeing Phantom works. The automated module demonstrated excellent health state classification results. The prognosis was also successfully performed however no data was available to validate the prediction. These algorithms were developed with consideration to sensor/processing limitations for potential onboard implementation. Many of the PHM elements presented here could also be adapted for other actuator types and applications.

Data-driven neural network methodology to remaining life predictions for aircraft actuator components

Abshact Aircraft hydraulic systems are used to actuate flight control surfaces (flaps, spoilers, ailerons, elevators, and rudder), thrust vectoring and reversing mechanisms, landing gear, cargo doors, and in some cases, weapon systems. Within these flight and mission critical hydraulic systems, the hydraulic pump is widely recognized to be the most critical component. The current paper discusses methods to fulfill the need for on-line pump diagnostics through the development of a prototype in-line, intelligent pump monitor for critical hydraulic pumps and motors. The approach includes performance modeling, signal processing and feature extraction, feature level fusion, automated classification, and knowledge fusion for estimating degradation through the collection of inline pump sensor data and onboard processing. The methods employed and an initial hardware and software realization are reviewed. The effects and tracking of pump wear and fatigue damage as witnessed from a series of endurance tests conducted at the Air Force Research Laboratory (AFRL) will be shown from the successful results of stimulating the in-line pump with digitized test data.

In-line health monitoring system for hydraulic pumps and motors

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A model-based approach to prognostics and health management for flight control actuators

Data-driven neural network methodology to remaining life predictions for aircraft actuator components

In-line health monitoring system for hydraulic pumps and motors