Anomaly Detection

In asset-intensive manufacturing operations, Overall Equipment Effectiveness (OEE) and quality are key competitive advantages that can have a significant impact on your bottom line. Traditional reactive and fixed-schedule approaches to maintenance and quality control are no longer yielding significant improvements, and companies must adopt technologies to get to the next level. TIM™ can facilitate the anomaly detection to improve equipment reliability, optimize maintenance cycles and improved product quality- all without the need for extensive machine learning or data science expertise.

How to spot anomalies in the time-series data collected during the manufacturing process that can act as early indicators for equipment malfunctions or quality degradation- while minimizing false alarms is where TIM™ comes into play.

Industrial environments consist of a high number of assets. Rather than having to build the models to obtain your goal one by one, TIM™can generate them automatic. High quality models over the in-sample training are generated. TIM™ learns the normal behaviour of a signal and then checks for any significant departures from this normality to find samples that are anomalous with respect to historical behaviour.

This process is intuitive as TIM™ models are transparent, and thus, describe the key drivers of the normal behaviour. The degree of normality is provided as a quantity called TIM™ anomaly indicator. This approach is useful for both, univariate and multivariate problems.

With TIM™  we bring scalability and transparency to your anomaly detection needs. It allows for smoother more predictable and thus more efficient operations of asset.

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