In-belt vibration monitoring of conveyor idlers and using wavelet package decomposition and artificial intelligence for early fault detection

Abstract

Conveyor systems make use of idlers that support the belt and its payload as it is circulated. These idlers have bearings to ensure lower friction between the idlers and the belt. These bearings do become contaminated with dust and dirt and bearings tend to fail or even seize, adding unwanted strain and stress on the belt. These idlers are monitored and replaced when needed to minimize the damage to the belt. There are several methods used to monitor the condition of the idlers. Thermal cameras are used to identify failing bearings that tend to run hotter than healthy bearings. Acoustic equipment exist that can capture the sound produced by the idler and processes it to indicate whether an idler is still working properly or when it is failing. These methods require an operator to travel the length of the belt, monitoring the idlers along the way. Vibrations have been used, with great success, to monitor idlers. An accelerometer is attached to the structure of the conveyor and the vibration signals are processed and from this a possible failing idler can be identified, either by an operator or an automated artificial intelligence system. However, the sensor can only monitor a few idlers close by and the cost of installing accelerometers along the entire length of a conveyor does make such a system infeasible. A method of using an accelerometer attached to the moving belt that travels over the idlers is investigated in this study. The vibration signals of the idler are captured as the accelerometer passes it and are then analyzed and used in a decision making system to identify and classify the idler bearing conditions. The accelerometer is attached at different positions across the width of the belt to investigate the possibility of only using one or two sensors to monitor all the bearings of the idlers across the width of the conveyor. Healthy bearings are tested against bearings with inner raceway, outer raceway and rolling element defects. Contaminated bearings are tested as well. Wavelet package decomposition is used to extract the bearing features and presents it to the intelligent decision making system. Neural networks and support vector machines are used with great success to identify and classify faulty bearings. The support vector machine monitoring system has a 100% accuracy in identifying and classifying faulty bearings, regardless of the sensor position and even when a localized payload is added. The system could not only identify a faulty bearing, but also classify the fault with 100% accuracy. These accuracies were obtained in a controlled experimental environment with a simplified test setup. The self-developed data acquisitioning system costs as much as one meter of steel reinforced rubber belt. There are some improvements needed before it could be implemented into a working conveyor, adding to the cost. A working in-belt idler monitoring system is not only plausible, but will be affordable as well.