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

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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

Man-made disasters

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

As a part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principles which integrates researched IVHM technologies in support of integrated intelligent vehicle management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems. The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of IIVM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: guidance and navigation; communications and tracking; vehicle monitoring; information transport and integration; vehicle diagnostics; vehicle prognostics; vehicle mission planning; automated repair and replacement; vehicle control; human computer interface; and onboard verification and validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for IIVM includes: 1) enhanced communications and telemetry, 2) sensors for radiation materials, 3) vehicle controls and dynamics, 4) flight mechanics and control, 4) embedded sensors for structural integrity of tanking systems, 5) evolutionary concepts for embedded sensor placement in tank systems, 6) real time operating systems, and 7) computer architectures for distributed processing for IVHM. This paper presents the IIVM framework and the IVHM technologies developed under NASA's NFFP pilot project

A framework for integration of IVHM technologies for intelligent integration for vehicle management

System engineering of launch vehicles and spacecraft is a challenging and complex undertaking. There are many diverse systems that must be integrated and balanced to produce an effective design. Th...

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Michael D. Watson

Papers

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

A framework for integration of IVHM technologies for intelligent integration for vehicle management

System Exergy: System Integrating Physics of Launch Vehicles and Spacecraft