How to Use Predictive Analytics to Monitor Sensor Health and Improve Data Quality

Applications for predictive analytics are endless, but a possible first step:  engineers can use analytics to monitor manufacturing sensors for reliability and operational performance.

Bad sensor data can mean lost product, downtime, compliance issues, and safety risks as well as a dirty data foundation for digital transformation and continuous improvement programs. Industrial organizations need to have good data that can be leveraged for operations, ad hoc analysis, and enterprise analytics.

Over time, manufacturing sensors tend to deviate, impacting processes and operations. But, it’s time consuming – and impossible for most organizations – to manually determine if and why sensors are working or failing ahead of increasing risk.

Predictive Analytics and Manufacturing Sensors

Engineers can employ a predictive analytics software such as Proficy Sensor Health to continuously monitor and analyze sensor data. Users can target anomalies and minimize their potential impact. The analytics app provides an easy way to automate the detection of bad sensors, where data is deviating from normal conditions.

When an anomaly is detected, the software can generate alarms to speed repairs, replacements, and recalibrations.

By using predictive analytics software to monitor sensor health, engineers can:

• Reduce downtime: Sensors are often used to provide indications that equipment is running correctly. Incorrect readings can lead to equipment failure or damage. Early detection of a sensor that is no longer giving accurate or consistent results can provide advance warning that enables maintenance to replace or recalibrate the sensor before the worst happens.

• Improve product quality and compliance: Sensors are often used for measuring the results of a product or to ensure the ambient surroundings of a process are within specification. If the sensors used to measure the product or environment are not accurate or functioning correctly, it can lead to a product being out of specification. Providing early warnings can reduce costs related product recalls or scrapped product.

• Ensure sensor data quality: Ensuring data quality in downstream analytics is part of IoT-fueled improvements. If the intent is more advanced use of analytics for a process, the need for ensuring data quality is critical.