Sensors and IoT Devices
Sensors are the backbone of predictive maintenance. They collect real-time data on various parameters such as temperature, vibration, pressure, humidity, operating times and distances. IoT devices facilitate the seamless transmission of this data to central analytics systems.
Type of Sensors:
Vibration Sensors: Used to monitor vibrations in motors, pumps, and other rotating equipment. Variations in vibration patterns often indicate mechanical issues such as misalignment or imbalance.
Thermal Sensors: Measure temperature changes in machinery. Overheating often indicates bearing failures or issues with lubrication.
Acoustic Sensors: Capture sound waves generated by machinery. Changes in acoustic patterns can indicate faults such as cracks or leaks.
Optical Sensors: Monitor light patterns and are used to detect surface defects, alignment issues, and other visual anomalies.
Data Collection and Storage
The vast amount of data collected by sensors needs to be stored in a structured format. Cloud storage solutions are often employed for this purpose, offering scalability and ease of access.
SSI SCHAEFER uses edge devices to access the real-time data at control level and then transfer it to the Computerized Maintenance Management System, the WAMAS Maintenance Center.
Data Enrichment:
Preprocessing: Raw data collected from sensors is enriched through preprocessing steps like filtering, normalization, and transformation. This makes the data more suitable for analytics and machine learning models.
Real-time Processing: Systems capable of real-time data processing provide immediate insights, allowing for timely interventions.
Advanced Analytics and Machine Learning
The collected data is analyzed using advanced algorithms and machine learning models. These technologies can identify patterns and correlations that indicate potential equipment failure.
At SSI SCHAEFER, global machine data is used in addition to the individual maintenance history, which is available via the WAMAS Maintenance Center. On this basis, deviating behavior in the form of anomalies, for example, can be detected.
Role of Algorithms:
Linear Regression: Used for predicting numerical values based on historical data.
Decision Trees: Employed for classification tasks and identifying fault patterns.
Neural Networks: Effective for deep learning tasks, particularly useful in recognizing complex patterns and anomalies.
Anomaly Detection: Algorithms specifically designed to identify deviations from normal operating parameters, signaling potential issues.
Machine Learning Models:
Supervised Learning: Involves training a model on labeled historical data to predict future outcomes.
Reinforcement Learning: Models improve their predictions through trial and error, learning from their past decisions.
Unsupervised Learning: Identifies hidden patterns in unlabeled data, useful for anomaly detection and clustering.
User Interface and Dashboards
The insights generated from data analysis are presented on user-friendly dashboards. These interfaces allow maintenance teams to monitor equipment health and receive alerts about potential issues in real-time.
Visualization Tools: Dashboards utilize various visualization tools like graphs, heat maps, and trend lines to present data intuitively.
Real-time Alerts: Immediate notifications enable quick responses to potential issues, minimizing downtime.
