How companies can leverage Industrial IoT infrastructure for anomaly detection?

Anomaly detection involves identifying patterns or behaviours that deviate from normal operating conditions and alerting operators or automated systems to take action. Industrial IoT infrastructure can be used for anomaly detection by monitoring sensor data from various equipment and systems in a manufacturing or industrial setting.

Anomalies can indicate equipment failures, production issues, and other problems that can affect efficiency, safety, and quality and thereby help companies save on increased operating costs owing to downtime in manufacturing process.

Bu using industrial IoT infrastructure, companies can implement sensors and other connected devices that gather data about equipment and processes in real-time. This data can then be analyzed using machine learning algorithms to identify patterns and anomalies. There are several steps involved for anomaly detection:

Identify the variables to monitor: Companies need to identify the key variables that are indicative of normal and abnormal behavior. This might include temperature, pressure, vibration, or other metrics depending on the equipment or process being monitored. It is important to have a clear understanding of what constitutes normal operating conditions for the equipment or system being monitored. This may involve analyzing historical data to identify patterns and trends.

Collect data: IoT sensors and devices can be used to collect data on these variables in real-time. This data can be stored in a centralized database (Historian) or the cloud for analysis.

Clean and preprocess data: Raw data can contain errors, missing values, or other issues that can affect the accuracy of anomaly detection. It’s important to clean and preprocess the data before using it for analysis.

Analyze the data: Machine learning algorithms can be used to analyze the data and identify patterns and anomalies. There are several types of algorithms that can be used, including clustering, classification, and regression or other statistical techniques and physics based models to identify outliers.

Notification: Once a certain anomaly is detected for a specific parameter pertaining to equipment, the information needs to be notified to the teams involved. There can be multiple ways this can be done either by using a mobile app, an email or an integration with a collaboration suite used by the company like MS Teams or Slack etc.

Automation: Companies can derive immense value once they integrate the anomaly detection tools with an existing plant maintenance software like SAP PMI or service management softwares. This integration can help them automate the process of creating a service request automatically once an anomaly is detected and thereby completely removing the human involvement and the latency.

Thus companies can leverage anomaly detection to reduce manufacturing downtime & improve efficiency.