MARATTO

review · Complex & Intelligent Systems

Prognostics: a literature review

2016173 citationsOpen accessKafr el-Sheikh University

In plain language

Integrated systems health management is an enabling technology designed to safeguard the safe and reliable operation of complex engineering systems. By spanning stages from design, analysis, and construction through to verification, operation, and maintenance, it helps lower processing times, operational durations, labour requirements, and overall costs, whilst increasing system availability and utility. Within this framework, prognostics represents one of the most beneficial yet demanding components. Estimating the remaining useful life of equipment through prognostic methods represents a major operational shift for systems health management. To streamline access to this broad field, an overarching synthesis outlines the essential foundations of prognostics. This consolidation covers the primary advantages, methodology types, practical engineering applications, and ongoing technical challenges, offering an accessible entry point for understanding how prognostics integrates into broader health management disciplines.

Key takeaways

  • Integrated systems health management supports the safety, reliability, availability, and cost efficiency of complex engineering systems across their lifecycles.
  • Prognostics is one of the most challenging and valuable elements within integrated systems health management.
  • Estimating the remaining useful life of equipment provides a major shift in operational maintenance strategies.
  • A structured synthesis gathers the essential benefits, approaches, applications, and challenges of prognostics into a single foundation.

Why it matters

Complex machinery requires constant oversight to prevent dangerous or expensive breakdowns. By predicting when components will fail before disruptions occur, engineering teams can cut operational expenses, save labour, and keep vital systems running safely. Consolidating the core principles, approaches, and challenges of prognostics helps both researchers and technical professionals quickly understand how to track and forecast equipment lifespan.

Commercialisation angle

Predictive health management systems can benefit operators of complex engineering assets seeking to lower maintenance overheads, reduce labour, and maximise equipment availability. However, because the text describes a literature review examining broader concepts, approaches, and challenges rather than a tested software tool or physical device, the findings represent early-stage conceptual knowledge rather than an applied, market-ready technology.

AI-generated from the published abstract. Always read the original work before citing.

Abstract

Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

Research topics

  • Machine Fault Diagnosis Techniques
  • Reliability and Maintenance Optimization
  • Fault Detection and Control Systems

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1007/s40747-016-0019-3

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.