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## Meet the editors

Germano Lambert-Torres received his Ph.D. in Electrical Engineering from the École Polytechnique de Montreal, Canada, in 1990. Currently, he is the director of research and development at PS Solutions and a member of the Scientific-Technical Council at Gnarus Institute, Brazil. Dr. Lambert-Torres has led research in intelligent systems, management of maintenance, and power system operation for many power industries in Brazil

and South America. He has also taught seminars and short courses in many countries worldwide at the Institute of Electrical and Electronics Engineers (IEEE), International Council on Large Electric Systems (CIGRÉ), and International Federation of Automatic Control(IFAC) conferences, universities, and research centers. He has supervised more than 120 MSc and Ph.D. theses and published more than 700 journal and technical conference papers. He is also the author/editor of ten books. Dr. Lambert-Torres is a fellow of the IEEE.

Erik Leandro Bonaldi received his Ph.D. in Electrical Engineering from Itajuba Federal University, Brazil, in 2006. He is the CEO of PS Solutions, where he coordinates dozens of employees producing metallic structures, parts, and devices for industries. The company also manufactures materials based on boiler making and machining. All processes are knowledge-intensive, with a demand for high quality. Dr. Bonaldi is currently the

head of the Scientific-Technical Council at Gnarus Institute, Brazil, where he is the coordinator of many research and development projects for Brazilian power and oil industries, verifying aspects of oil quality and producing sensors and systems for automation control. His research interests include maintenance management, industrial electronic processes, automation, predictive maintenance techniques, and artificial intelligence methodologies.

Levy Ely de Lacerda de Oliveira received his Ph.D. in Electrical Engineering from Itajuba Federal University, Brazil, in 2006. He is currently the CTO at PS Solutions, where he develops equipment for predictive maintenance, sensors for many purposes for Industry 4.0, and electronic systems (hardware, firmware, and software) for monitoring electric machinery. He is also a research associate at the Gnarus Institute, Brazil, coordinating

developments of data acquisition systems, digital signal processing, and microcontrollers for electronic devices, electronic power systems, power converters, and applications of adaptive and intelligent control in industrial problems. His research interests include DSPs and FPGA developments, condition-based electric machinery maintenance, vibration analysis, and digital signal processing.

## Contents


Preface

Modern companies are increasingly trying to reduce costs while maintaining the quality of their services and the time spent producing them. This practice is associated with strict control of maintenance management. To this end, maintenance management information systems have been developed to scan each step of the processes, measure quantities, and plan interventions integrated into the production process to

The main objective of maintaining maintenance processes is to ensure the quality and continuity of production processes. It should be done by examining all aspects of the manufacturing routine, identifying bottlenecks, and evaluating the criticality of each component or subsystem in the overall manufacturing process of the final product. This management should consider repair costs (or replacement) and miscellaneous expenses, planning the work effectively, with reduced repair times and clear instructions for the

Several computational tools for managing maintenance processes help the maintenance manager make more proactive decisions. These tools provide data and performance hints from the various processes, the compilation and analysis of data, trend reports, history of past maintenance, and history of similar equipment (or processes),

Maintenance processes have undergone many changes over time. The first phase was simply corrective maintenance ("repair it when it broke"), in which a component is replaced when it breaks. This type of maintenance causes severe problems for production because the breaks usually occur during the production peaks, thus generating instabilities in quality. The second maintenance phase involves preventive maintenance (also called programmed maintenance), which makes exchanges of components after a specific time. There are two main types of preventive maintenance: time-based maintenance and usage-based maintenance. Time-based preventive maintenance is typically applied to equipment critical in the production process that, once damaged, severely impacts production. In usage-based maintenance, the exchange (or even only one inspection) is done based on production hours or cycles of production. This type of maintenance, which still occurs today in industries, also causes financial problems and unnecessary costs because parts and components can be exchanged in good condition, or unnecessary inspection actions can be performed. The advantage of this type of maintenance process over the previous one is that this process can be programmed, thus avoiding creating problems for production.

Then, with the cheapness of the measuring equipment, there was the possibility of better system observability being used. The next phase of the maintenance process is predictive maintenance. In this process, several quantities are observed to monitor each component's wear. By monitoring the amounts, this type of maintenance

optimize production and maintenance procedures.

procedures to be performed.

among others.
