The developing field of integrated vehicle health management

The goals that are being set for aviation growth in the near future, combined with the growth in service provision, are unattainable without active health management of airplanes. Numbers associated with door to door travel time and accident rates, coupled with availability demands to provide cost-effective transport, simply do not allow time for unscheduled maintenance. We are therefore going to experience a step jump in the take up of Integrated Vehicle Health Management (IVHM) on these platforms in order to give accurate warning of sub-system and component degradation, allowing for maintenance to be carried out in a timely, scheduled, manner. This paper describes the development of IVHM, covering emerging services, standards, technology and IVHM as used in various industry sectors. This will lead to the commercial picture of today with the top level goals that are being set, providing the business push for technology and its adoption. Examples of research being conducted in the field will be shown, to support the claim that real progress is being made, with implementation of this technology on the horizon.http://www.aerojournalindia.com/journal.htm

Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

Physics-based prognostic modelling of filter clogging phenomena

In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results

A simple state-based prognostic model for filter clogging

In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition

Use of COTS functional analysis software as an IVHM design tool for detection and isolation of UAV fuel system faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADe™ (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis

Integrating IVHM and asset design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process