This paper provides an in-depth study of vibration sensors for condition monitoring in heavy machinery. The authors review the use of vibration-based condition monitoring, focusing on data collection methods and devices. They investigate various accelerometers and their technologies for vibration measurement in heavy machinery. The review highlights the importance of vibration-based condition monitoring in reducing downtime, maintenance costs, and safety risks. It also discusses the challenges and advantages of using vibration data for predictive maintenance. The study emphasizes the need for advanced accelerometers to accurately measure vibrations in complex machinery environments. The authors analyze different types of accelerometers, including piezoelectric, PCB, wired/wireless, MEMS, and triaxial accelerometers. They also discuss the use of simulations and existing datasets in vibration-based condition monitoring, highlighting their advantages and limitations. The review includes a detailed analysis of various heavy machinery components, such as gearboxes, bearings, turbines, transportation infrastructure, and electric motors, and their vibration data collection methods. The study concludes that vibration-based condition monitoring is essential for improving the efficiency, reliability, and safety of heavy machinery operations. The authors recommend the use of advanced sensors and data analysis techniques to enhance predictive maintenance strategies and reduce unplanned downtime.This paper provides an in-depth study of vibration sensors for condition monitoring in heavy machinery. The authors review the use of vibration-based condition monitoring, focusing on data collection methods and devices. They investigate various accelerometers and their technologies for vibration measurement in heavy machinery. The review highlights the importance of vibration-based condition monitoring in reducing downtime, maintenance costs, and safety risks. It also discusses the challenges and advantages of using vibration data for predictive maintenance. The study emphasizes the need for advanced accelerometers to accurately measure vibrations in complex machinery environments. The authors analyze different types of accelerometers, including piezoelectric, PCB, wired/wireless, MEMS, and triaxial accelerometers. They also discuss the use of simulations and existing datasets in vibration-based condition monitoring, highlighting their advantages and limitations. The review includes a detailed analysis of various heavy machinery components, such as gearboxes, bearings, turbines, transportation infrastructure, and electric motors, and their vibration data collection methods. The study concludes that vibration-based condition monitoring is essential for improving the efficiency, reliability, and safety of heavy machinery operations. The authors recommend the use of advanced sensors and data analysis techniques to enhance predictive maintenance strategies and reduce unplanned downtime.