Machine Condition Monitoring: Predicting Mechanical Wear and Tear

On Reddit, 558,000 people subscribe to r/CatastrophicFailure, a crowd-sourced display of machines and other systems failing with dramatic results. There are explosions, sometimes injuries and the ringing knowledge of some very expensive repairs.

While the videos of these events can be darkly fascinating, their consequences can be horrific. As a result, operations managers and engineers have developed high-tech IIoT machine condition monitoring systems that detect the warning signs of failure. These risk mitigating tools have been essential to asset management.

What is Machine Condition Monitoring?

Machine Condition Monitoring (MCM) is a digital oversight process for predictive maintenance that uses IIoT edge technology. It tracks specified criteria that indicate mechanical wear and tear or machine electrical issues.

Using the collected information, an MCM system alerts machine specialists about potential problems and their locations on the machine. This protocol helps prevent catastrophic failure, decreases workplace injury risks, mitigates downtime costs and reduces expensive repairs.

Manufacturing machine condition monitoring is the most common application of the system. However, energy sector enterprises or other industries with heavy machinery stand to gain the most from the technology. The remote monitoring required by oil and gas, nuclear, solar and wind energy sites is easily fulfilled by the automated nature of condition monitoring. Anywhere regular access for monitoring is inhibited could benefit from machine condition monitoring.

Predictive Maintenance

Comprised of sensors, edge devices and a cloud service, an MCM system automates supervision and turns repairs into predictive maintenance. Although catastrophic failure is the worst-case problem, it is not the primary goal for asset managers. Rather, machine condition indicates reliability and efficiency. By scheduling maintenance at optimized intervals that work within production and repair schedules, these systems increase overall efficiency and extend the life of assets.

Advanced sensors — especially flexible hybrid electronic sensors (FHE) — are configured to measure conditions that indicate change of status. For mechanical devices, temperature, vibration and acoustic changes correlate the most to impending repairs. For electrical systems, voltage sensors offer the clearest picture of energy use and malfunctions. By communicating over low-power networks and having a simple function, these sensors have extended battery lives.

Edge devices are localized command, control and, sometimes, analytics machines connected to the internet. This hardware functions as a halfway point between the cloud and the sensor. It collects information from the sensors and delivers it to the cloud. For remote monitoring, that information may be delivered continuously or intermittently over cellular.

The cloud service used in machine condition monitoring determines the effectiveness of data collection. It allows for remote reliability management as well as remote asset management. Even when a device may be good shape, managing the machine’s condition data is a proactive way to schedule maintenance and ensure machine assets have an accurate usage history.

Applications of Machine Condition Monitoring

Machine condition monitoring in wind turbine management offers one of the clearest pictures of the system’s benefits. Wind turbine gear boxes are complex, massive systems. As well, they are hard to access, making them notoriously difficult, and dangerous, to service.

By installing vibration and FHE temperature sensors, turbine mechanics can remotely monitor the machine’s function. If anything is loose, increased vibration beyond the standard levels will be immediately picked up. At this point, the machine can be turned off remotely if the information indicates a serious error. However, if the event disappears, the information can be logged in the cloud analytics program.

If the event appears again, the cloud program can correlate it to the previous record as well as other information, such as runtime and weather conditions. This will create a history that indicates how effective the turbine is during specific circumstances. It will also establish a baseline for the machine so that indicated events can be assessed for risk.

Ultimately, this dangerous maintenance can be scheduled on safer terms. By using the collected data for prognosis, rather than diagnosis, wind farmers can use predictive maintenance on wind turbines.