IIoT Devices and Sensors Used in Predictive Maintenance
IIoT uses a wide range of equipment and sensors, including ultrasonic detectors, accelerometers, and temperature sensors, to mention a few. Numerous characteristics, including but not limited to temperature, vibration, pressure, and sound levels, can be measured by these sensors. When these sensors are combined with edge computing capabilities, they can process raw data first and then send the cleaned data to the cloud for additional analysis.
Temperature Sensors: These are crucial for machines that are sensitive to temperature fluctuations. Overheating is often a sign of malfunction or impending failure.
Vibration Sensors: Often used in rotating machinery like turbines and compressors, these sensors can detect imbalances or misalignments before they become critical.
Acoustic Sensors: These can pick up ultrasonic noises emitted by failing equipment, which are inaudible to the human ear but indicative of issues like gas leaks or electrical discharge.
Pressure Sensors: Crucial in hydraulic systems, these sensors monitor fluid pressure to ensure it remains within operational limits.
Humidity Sensors: In environments where moisture levels are critical, such as food processing or pharmaceuticals, these sensors can provide valuable data.
Flow Meters: Used extensively in liquid processing industries, these devices can identify irregularities in the flow rate, which may signify problems in the system.
Electrical Current Monitors: These can detect anomalies in electrical consumption, signaling issues like overloads or potential circuit failures.
Once the IIoT devices collect the data, it is transmitted to a centralized database, often in the cloud but sometimes on-premises, based on the specific requirements of the business. The data is then processed and analyzed using advanced analytics platforms that employ machine learning algorithms and data science techniques.
These algorithms are trained to recognize complex patterns and trends within the data, which human analysts might find difficult to spot. By learning from historical machine behavior and real-time data, the system can identify anomalies symptomatic of mechanical problems. When such abnormalities are detected, alerts are generated to notify the maintenance teams.
Additionally, the predictive maintenance system may also provide detailed recommendations on what corrective actions should be taken, whether it’s a minor adjustment or a more involved part replacement. This ensures that not only is the problem identified in advance, but it can also be accurately diagnosed and fixed before it leads to a costly shutdown.
