Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Wireless Sensor Network based Machine Condition Monitoring <s> Impact of Maintenance Financial Implications and Cost Justification Role of Maintenance Organization Benefits of Predictive Maintenance Machine-Train Monitoring Parameters Predictive Maintenance Techniques Vibration Monitoring and Analysis Thermography Tribology Process Parameters Other Methods Visual Inspection Operating Dynamics Analysis Failure-Mode Analysis Establishing a Predictive Maintenance Program A Total Plant Predictive Maintenance Program Maintaining the Program World-class Maintenance <s> BIB001 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Wireless Sensor Network based Machine Condition Monitoring <s> Need for Condition Monitoring and Maintenance Management in Industries. Cost Effective Benefits of Condition Monitoring. Condition Monitoring Techniques. Machinery Fault Diagnosis. Vibration Condition Monitoring. Condition Monitoring of Gear Systems. Condition Monitoring of Hydraulic Systems. Condition Monitoring of Pneumatic Systems. Condition Monitoring of Electrical Machinery. Condition Monitoring of Turbomachinery. Wear and Oil Debris Monitoring. Artificial Neural Networks in Condition Monitoring. Infrared Thermography in Condition Monitoring. Expert Systems in Condition Monitoring. Corrosion Monitoring. Environmental Monitoring - Legal Aspects. Cost Effective Benefits of Maintenance. Reliability Centred Maintenance. Total Productive Maintenance. Modern Maintenance Management Techniques. <s> BIB002 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Wireless Sensor Network based Machine Condition Monitoring <s> Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher’s community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis. <s> BIB003
Failure induced by various factors like improper installation, unsuitable temperature, corrosion, abrasion, fatigue, oil debris and occurred on components (such as motors, generators, engines, pumps, bearings, gears and shafts) of engineering systems can unexpectedly cause machinery to collapse and lead to significant losses for industries BIB002 . Therefore, the maintenance of machinery plays a vital role in industries and has been studied by researchers for decades. There are three common maintenance strategies arranged in order of progression: breakdown maintenance, preventive maintenance and predictive maintenance BIB003 . Breakdown maintenance is an unplanned maintenance way to maintain the damaged machines through the component repair or direct replacement, which seriously affects the continuity of industrial production and brings dramatically high maintenance costs. As a planned maintenance form, preventive maintenance is carried out according to the regular and periodic inspection and maintenance to detect and avoid issues before failures or breakdowns happen. Although this approach reduces the frequency of catastrophic failures and increases the reliability of systems, the operation of setting an optimal inspection period is complicated and frequent change of components is costly. In order to improve the stability of mechanical systems and reduce the economic losses and costs induced due to failures and breakdowns, an intelligent and effective maintenance strategy, predictive maintenance, needs to be established to prevent the occurrence of failures by relying on many techniques, such as thermal imaging, lubricant analysis and vibration characteristics BIB001 . Condition-based maintenance (CBM) is an effective and efficient strategy of predictive maintenance. It relies on the assessment of machine operating condition to determine whether the components require maintenance or not to enhance the stability and reliability of mechanical systems, minimise the labour and material resource costs, as well as improve the operating safety. First, raw data sets representing the condition of machinery (like temperatures, pressure, torques, vibrations, displacements, acoustics and acoustic emission) are collected by a variety of sensors (like thermocouples, pressure gauges, microphones and accelerometers) and saved in the storage devices through data acquisition devices. Then, the data sets are processed to present the machine conditions by various techniques. These signal processing methods in condition monitoring mainly consist of feature extraction, modelling and machine learning as shown in Table 8 . Finally, decision making can give the recommendation of maintenance actions to prevent deterioration through fault diagnosis which is a process of determining health, faults and even their severity of systems being monitored. 