Identifying faults in industrial components with wireless sensors 

September 27, 2022
Identifying faults in industrial components with wireless sensors 

The identification of failures in industrial components has gained a new ally when it comes to continuous monitoring of equipment: the collection of vibration and temperature data through wireless sensors or wireless sensors that follow the equipment activity 24 hours a day. This data, when analyzed and monitored continuously, ensures an effective strategy for planning maintenance of the simplest to the most critical assets.  

Predictive maintenance techniques are increasingly present in maintenance management activities and industrial sectors. The truth is that the continuous monitoring of assets and equipment, through sensors and software, have assumed a major role in decision making based on data easily collected and analyzed in the palm of your hand or in a AMC (Asset Monitoring Center), for example. 

Acting as allies of the manager and the maintenance team, the use of sensors and software with easy-to-view panels optimizes time, identifying priorities for decision making, thus reducing costs and losses, aiming at a controlled operational flow with the automation of some activities. 

Monitoring Complex Assets 

Conveyor Belts are Class A assets in a mining or industrial process, i.e. equipment that cannot stop without affecting an entire production line. Because of their size, there are many points to be monitored and/or taken into consideration when we talk about planning and maintenance.  

Through this initial analysis, seeking to reduce work hours and avoid a manual process that is not in compliance with NR 12, wireless sensors were installed on the conveyor belt extension. The Dynaloggers as soon as they are applied to the equipment seek to map vibration and temperature aspects that act in the normality of its operation.  

After collecting this information, the system generates graphics that point out any and all unusual activity or activities that could result in a catastrophic failure or unscheduled downtime. 

All of this is only possible through maintenance and asset-specific metrics, such as the conveyor: the motor, gearbox, bearings, carpet, drums, and other related problems.   

Check out the following three cases identified by the technical team in the field, showing how technology is a great ally in automated data collection, helping the asset management and bringing benefits in industrial processes.

1- Monitored low rotation of a conveyor belt bearing  

The failure in a conveyor belt bearing was another failure previously identified through Dynamox’s remote prognosis. 

Through the visualization forms of anomalies in spectral analysis and waveform autocorrelation, available in the DynaPredict Platform, it was evident in the graphics the change in the asset’s behavior, pointing out the low bearing rotation, for early corrective action of the failure and possible evolution of the anomaly. 

Both cases occurred on the same asset, but at different and distant points due to their extent.  

2 – Looseness in the KM nut of bearing attachment  

Thinking of avoiding a production stoppage, Belém Bioenergia has invested in wireless sensors to continuously monitor the machine and thus signal anomalies in advance. 

Through the reading of the spectra under acceleration, a change in the equipment’s behavior was identified, indicating rotational looseness in the bearing.  

With the in-depth analysis of the spectra generated by the DynaPredict platform, technical reports pointed out a KM nut looseness, which needed correction. 

Simple maintenance, such as tightening the bolts, solved the problem in a scheduled stop that was planned when the need for repair was identified.  

After this process, the machine was back to operating with low vibration and avoiding bearing failures. This process does not generate “corrective forces”, it is included in the preventive backlog, and does not generate differentiated costs. 

3 – Vibration signals an engine mounting problem

Wireless sensors installed on the machine capture its “natural” vibration and normality levels. The collected data is analyzed and presented in specialized panels that work as an “assistant”. Taking this into consideration, this work with vibration and temperature analysis metrics of a conveyor belt resulted in pointing out a critical state on the dashboard screen. 

From the criticality signaling, a detailed study was done that pointed out the vibration increase in speed to more than 11x the original vibration, detecting the looseness of the mounting screws of the tripper drive motor. 

Based on this data, a scheduled shutdown was planned and the engine intervention and correction procedure was performed, where the team corrected the failure and the equipment returned to operate with normal vibration. The whole process resulted in zero exposure of the team in the field and significantly reduced the costs that an unplanned stoppage could generate. 

The use of sensors and gateways at Vale’s plant in Parauapebas helped the maintenance team to identify the fault, where access to the equipment is restricted for safety reasons. The machine goes through the blocking process (where the repair happens while it is stopped) in time to schedule the action. 

For Luis Silva, Maintenance Technician, who participated directly and indirectly in all projects, “the tool greatly facilitates the analysis of the assets’ condition. The platform’s graphics, logs, and generated reports are an important differential of the solution. 

Dynamox’s wireless sensors are responsible for collecting data from the predictive maintenance solution, DynaPredict, and through them significant results are mapped for the maintenance department of industries that seek to invest in data analysis and continuous and remote monitoring of the plant. 

Keep browsing and see what the DynaPredict has to add to your industry in failure detection and analysis, contributing to an assertive and efficient asset management. 


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