Maintenance Audits Handbook: A Performance Measurement Framework explores the maintenance function and performance of an organization, and outlines the key aspects required for an effective and efficient maintenance performance measurement (MPM) system. Incorporating different aspects of traditional literature and considering various frameworks on the subject, it examines the auditing process as well as the use and development of maintenance metrics. It identifies different frameworks and models showcasing how MPM systems should be implemented as well as the values that are created when different frameworks are used. The book presents performance indicators within a framework that classifies and sorts according to functional and hierarchical aspects. It introduces techniques that can help determine the right set of performance indicators. It also outlines a process that combines both numerical indicators with the classical result of massive questionnaires successfully incorporating both the quantitative and qualitative aspects of maintenance performance. In addition, the author provides examples of MPM frameworks that are used in organizations with condition-based, vibration-based, and reliability-centered maintenance. A useful handbook for students and maintenance professionals, this book provides readers with an understanding of how to Align the organizational strategy to the strategies of the maintenance function Link the maintenance performance measures to the different hierarchies of the organization and establish effective communication between them Translate the MPIs at operational level to the corporate level (to create value for the whole organization and its customers) Identify the weaknesses and strengths of the implemented maintenance strategy Maintenance Audits Handbook: A Performance Measurement Framework provides readers with a sound foundation for developing and measuring a comprehensive maintenance improvement strategy using qualitative and quantitative data, and serves as an ideal resource for maintenance/mechanical engineers, maintenance/performance/business/production managers and industry professionals involved in maintenance.

Maintenance Audits Handbook: A Performance Measurement Framework explores the maintenance function and performance of an organization, and outlines the key aspects required for an effective and efficient maintenance performance measurement (MPM) system. Incorporating different aspects of traditional literature and considering various frameworks on the subject, it examines the auditing process as well as the use and development of maintenance metrics. It identifies different frameworks and models showcasing how MPM systems should be implemented as well as the values that are created when different frameworks are used. The book presents performance indicators within a framework that classifies and sorts according to functional and hierarchical aspects. It introduces techniques that can help determine the right set of performance indicators. It also outlines a process that combines both numerical indicators with the classical result of massive questionnaires successfully incorporating both the quantitative and qualitative aspects of maintenance performance. In addition, the author provides examples of MPM frameworks that are used in organizations with condition-based, vibration-based, and reliability-centered maintenance. A useful handbook for students and maintenance professionals, this book provides readers with an understanding of how to Align the organizational strategy to the strategies of the maintenance function Link the maintenance performance measures to the different hierarchies of the organization and establish effective communication between them Translate the MPIs at operational level to the corporate level (to create value for the whole organization and its customers) Identify the weaknesses and strengths of the implemented maintenance strategy Maintenance Audits Handbook: A Performance Measurement Framework provides readers with a sound foundation for developing and measuring a comprehensive maintenance improvement strategy using qualitative and quantitative data, and serves as an ideal resource for maintenance/mechanical engineers, maintenance/performance/business/production managers and industry professionals involved in maintenance.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource: Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques Considers the merits of each technique as well as the issues associated with real-life application Covers classification methods, from neural networks to Bayesian and support vector machines Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB (R) code for algorithm testing Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.

Industry 4.0 refers to fourth generation of industrial activity characterized by smart systems and internet-based solutions. This book describes the fourth revolution based on instrumented, interconnected and intelligent assets. The different book chapters provide a perspective on technologies and methodologies developed and deployed leading to this concept. With an aim to increase performance, productivity and flexibility, major application area of maintenance through smart system has been discussed in detail. Applicability of 4.0 in transportation, energy and infrastructure is explored, with effects on technology, organisation and operations from a systems perspective.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource: Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques Considers the merits of each technique as well as the issues associated with real-life application Covers classification methods, from neural networks to Bayesian and support vector machines Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB (R) code for algorithm testing Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.