Anomaly Detection Process Case Study

IoT Analytics, the Data Science process within Industry 4.0

It’s called anomaly detection and it’s a Data Science solution that can flag any anomalous data in corporate plants. Here’s why it’s important for the Smart Factories of the present and future.

The main task of IoT technologies is to integrate digital tools with machinery to enable faster management of issues. This includes the practice of anomaly detection, the identification of any anomalies and data that deviate from the usual state of normality.

Choosing to rely on this analysis means having a continuous monitoring of the performance of your production equipment in the company. At the same time, anomaly detection also helps to understand which phenomena generate errors in the normal processes of the machinery, thus speeding up the resolution procedures.

As exposed in the case study, the starting hypothesis was to study the “normal” trend of some plants over time, in order to precisely set operating standards and allow anomaly detection to report any deviations.

In a similar context, it was therefore necessary to set up a preventive work, which analyzed all the phenomena related to the machines present in the plant. In this sense, BitBang has collected over time data such as temperature, pressure, vibrations, sound waves and much more. This data collection allowed us to have an overall picture of the data that regulate the normal operation of the plant.