Preface

Industries require maintenance to ensure the correct operation of engines, components, structures, etc. Any failure, i.e. termination of the ability of an item to perform a required function, generates downtime, costs, risks for personnel, etc. High competitiveness in the current industry does not lead these failures to the firms.

Advances in information and communication systems, together with technologies, lead the industry to incorporate new sensors, condition monitoring systems, etc. They also require advanced analytics to format, save, and analyze these signals and information, from qualitative and quantitative point of views.

To reduce failure occurrence probabilities, a correct maintenance task is required. British Standard, BS EN-13306:2017 defines maintenance as "managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function. Technical maintenance actions include observation and analyses of the item state (e.g. inspection, monitoring, testing, diagnosis, prognosis, etc.) and active maintenance actions (e.g. repair, refurbishment)." Correct maintenance support for a maintenance organization to carry out the correct tasks is called maintenance supportability.

There are a large number of maintenance types:

Corrective maintenance is the most common type, and is done when the failure appears. When it is delayed it is defined as deferred corrective maintenance; in other cases it is called immediate corrective maintenance.

Preventive maintenance is done at certain times or according to criteria to reduce the probability of failure. Predetermined maintenance is set according to time intervals or use of an item.

Scheduled maintenance is done as predetermined maintenance or in a time schedule established previously.

Condition-based maintenance is carried out with regard to the item status and is set generally by sensors, testing, and analytics.

Predictive maintenance is the maintenance task done according to the predicted item condition to avoid failure.

According to EN 13306:2010, maintenance management is defined as "all activities that determine the maintenance objectives, strategies and responsibilities, and implementation of them by such means as maintenance planning, maintenance control, and the improvement of maintenance activities and economics." The maintenance strategy is set to achieve objectives, fixed by costs, availability, safety, reliability, etc. The maintenance strategy should be set by maintenance management from a responsibility point of view, considering availability, safety of personnel,

the environment, other mandatory requirements associated with the item, item durability, and final product quality taking into account the cost and any influence to the environment. Procedures, activities, resources, and time are considered in the maintenance plan.

The key indicators are found in European Standard EN 15341:2007. The objectives of key indicators are to measure the status, compare (internal and external benchmarks), diagnose (analysis of strengths and weaknesses), and identify objectives, and define targets to be reached, plan improvement actions, and continuously measure changes over time. There are three main groups of indicators: economic, technical, and organizational. They are set considering endogenous (company culture, industry, lifecycle of the components, criticality, etc.) and exogenous (location, society, culture, market, laws, regulations, etc.) variables.

This book presents the main concepts, state of the art, advances, and case studies of fault detection, diagnosis, and prognosis. This topic is a critical variable in industry to reach and maintain competitiveness. Therefore, proper management of the corrective, predictive, and preventive politics in any industry is required. This book complements other subdisciplines such as economics, finance, marketing, decision and risk analysis, engineering, etc.

The book presents real case studies in multiple disciplines. It considers the main topics using prognostic and subdiscipline techniques. It is essential to link these topics with the areas of finance, scheduling, resources, downtime, etc. to increase productivity, profitability, maintainability, reliability, safety, and availability, and reduce costs and downtime. Advances in mathematics, modeling, computational techniques, dynamic analysis, etc. are employed analytically.

Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support the analysis of prognostic problems with defined constraints and requirements.

The book is intended for graduate students and professionals in industrial engineering, business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying maintenance or needing to solve large, specific, and complex maintenance management problems as part of their jobs. The work will also be of interest to researches from academia.

> **Fausto Pedro García Márquez** Ingenium Research Group, Universidad Castilla-La Mancha, Ciudad Real, Spain

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**Chapter 1**

**1. Introduction**

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Introductory Chapter:

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autoregressive integrated moving average (ARIMA) [9], etc.

*Fausto Pedro García Márquez*

Prognostics - An Overview

Prognostics, in general, can be defined as "knowledge beforehand". Prognostics is usually identified with medical issues. Nowadays, due to the new advances in technologies and information systems, prognostic is beginning to be employed in

The main key indicators are given by European Standard EN 15341:2007 [1]. The objectives of the key indicators are to measure the status, compare (internal and external benchmarks), diagnose (analysis of strengths and weaknesses), identify objectives and define targets to be reached, plan improvement actions and continuously measure changes over time. There are three main groups of indicators: economic [2], technical [3] and organisational [4]. They are set considering endogenous (company culture, industry, life cycle of the components, criticality, etc.) and exogenous (location, society culture, market, laws, regulations, etc.)

Prognostics requires also of advance analytics in order to format, save and analyse these signals and information, from qualitative and quantitative points of view. *Model-based approach* takes into account the state prediction achieved through physics or system models, the following being mainly employed: model based on detection and isolation [6]. Hybrid models, extended Kalman filtering and particle filtering [7, 8]. *Data-driven approach* is also a state prediction with criteria evaluation, where the state prediction is achieved through regression or stochastic process modelling. The most important are autoregressive moving average (ARMA) or

Nowadays the information from an item or person is getting more and more, with more variables, complex, etc. The large amount of data requires to be analysed, considering the heterogeneity, noise accumulation, spurious correlations, and incidental endogeneity of the data. It does that new approach and algorithms based on artificial intelligence which will be appearing; Artificial Neural Network [10]; Fuzzy Logic System [11]; Hidden Markov Model [12]; Support Vector Machine [13], Relevance Vector Machine (RVM); Gaussian Process Regression [14], Multivariate Logistic Regression in general form, K-Means Clustering, Fuzzy Logic-Based Algorithms and Bayesian Belief Network, etc. Some algorithms can be applied together with the above-mentioned methods, e.g. gradient descent, alternating least squares, collaborative filtering, SVM kernel, belief propagation, matrix factoriza-

The next generation of approaches will require to process Big Data. Big Data is one of the central and influential research challenges for the 2020 Horizon, where the quantity of world data will be 44 times bigger in the next few years (0.8–35
