Servitization and Physical Asset Management by Michael John Provost;

Author:Michael John Provost; [Неизв.]
Language: eng
Format: epub
Publisher: SAE International

Prognostics

Prognostics is the process of predicting the time at which a component will no longer perform a particular function. This is usually expressed in terms of remaining useful life, which can be either time- or usage-based. The presentation by Fatih Camci (Camci 2013) explains the basics of prognostics very well, while the presentation by Lodovico Menozzi (Menozzi 2013) goes into more detail. The book by George Vachtsevanos and others (Vachtsevanos et al.2006) is a comprehensive review of diagnostic and prognostic techniques, taking a systems approach, including discussion of sensors and logistics and some case studies. Victoria Catterson’s presentation (Catterson 2012) discusses the use of linear regression with upper and lower bounds to predict when parameters reach thresholds. The paper by Allan Volponi and others (Volponi et al.2004) discusses the fusion of data from disparate sources to obtain more accurate and comprehensive diagnostic and prognostic information regarding the health of aircraft engines. The article by Moritz von Plate and others (von Plate et al.2015) discusses seven basic steps that should be taken to successfully apply prognostics.

Humaware (www.humaware.com) has developed CFAR-Autotrend, a specialized tool for the analysis of Health and Usage Monitoring System (HUMS) data gathered from helicopters that has a very low false alarm rate. The technology is also applicable to rail, marine, petrochemical, and other assets. The papers by Kenneth Pipe (Pipe 2006 , 2008a, 2008b, 2008c, 2009, 2013) describe the methodology, features, and functionality of this and related maintenance management software. Two papers by the UK Civil Aviation Authority (CAA) (CAA 2012a, 2012b) demonstrate the application of Advanced Anomaly Detection (AAD) methods successfully developed and applied to helicopter HUMS data by General Electric (GE): they show that the techniques used are both effective and practical.

The paper by Andrew Jardine and others (Jardine et al.2006) summarizes and reviews recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition-Based Maintenance (CBM), with emphasis on models, algorithms, and technologies for data processing and maintenance decision-making. The authors also discuss different techniques for multiple sensor data fusion and conclude with a brief discussion on current practices and possible future CBM trends.

The paper by Joanna Sikorska and others (Sikorska et al.2011) discusses business issues that need to be considered when selecting an appropriate Remaining Useful Life (RUL) modelling approach and presents classification tables and process flow diagrams to help select appropriate prognostic models for predicting the RUL of assets within a specific business environment. The paper also explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarizes how each has been applied.

The Prognostics and Health Management Society (PHM Society: www.phmsociety.org) is a nonprofit organization dedicated to the advancement of PHM as an engineering discipline. As well as producing an online journal (the International Journal of Prognostics and Health Management (IJPHM)), the society also runs an annual conference. The society’s conference proceedings (Bregon and Saxena 2012; Celaya et al.2011; Roychoudhury et al.2012; Sankararaman and Roychoudhury 2013; Daigle and Bregon 2014; Bregon and Daigle 2014) contain much useful information.

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