In order to guarantee the dependability and effectiveness of industrial
machinery, real-time gearbox malfunction detection is extremely important.
Traditional approaches to condition monitoring systems sometimes rely on
time-consuming human inspections or routine maintenance, which can result in
unanticipated failures and expensive downtime. The rise of the industrial
Internet of things (IIoT) in recent years has paved the way for more
sophisticated and automated monitoring methods. An IIoT-based condition
monitoring system is suggested in this study for real-time gearbox failure
detection. The gearbox health state is continually monitored by the system using
sensor data from the gearbox, such as temperature, vibration, and oil analysis.
Real-time transmission of the gathered data is made to a central monitoring hub,
where sophisticated analytics algorithms are used to look for any flaws.
This study’s potential to improve the dependability and operational effectiveness
of industrial gear is what makes it so significant. Real-time defect
identification makes it possible to undertake maintenance tasks preemptively,
avoiding catastrophic failures and cutting down on downtime. This reduces not
just the expenses of unanticipated maintenance but also boosts general
productivity and client happiness. The uniqueness of this study comes from the
way sophisticated analytics and IIoT technologies were used to find gearbox
defects. Despite the literature’s exploration of IIoT-based condition monitoring
systems, this work focuses especially on gearbox defect detection, which
presents special difficulties because of complicated mechanical dynamics and the
existence of several failure scenarios. The suggested methodology provides a
thorough and automated method that can precisely identify and diagnose gearbox
faults, leading to timely maintenance actions and increased operational
reliability. Overall, employing IIoT-based condition monitoring, this work
offers a unique and useful method for real-time gearbox failure diagnosis. The
results of this study can help improve industrial maintenance procedures, which
will enhance machinery performance and decrease downtime across a variety of
industries, including manufacturing, energy, and transportation.