Improving anomaly detection and prevention: What is your data telling you?
By Ruban Phukan, Co-Founder of Progress DataRPM and VP, Product, Progress The pace of digitisation means manufacturers today are under growing pressure to deliver perfect products in increasingly shorter timeframes, and at a lower cost. They can’t afford unplanned interruptions, unforeseen failures, or unexpected breakdowns, nor can they afford to wait until the quality check stage to identify issues that could have been avoided during the production line. According to Vanson Bourne, 82% of companies have experienced at least one unplanned downtime outage over the past three years, which can cost anywhere from $US50k-$150k per hour up to $US2 million for a major outage on an industrial critical asset. Industry research shows more than a third of the manufacturers lose 1-2% of their annual sales to scrap and rework. Data to the rescue! In order to reduce downtime, improve operational efficiencies and quality, manufacturers are heavily investing in data-led technologies. The Industrial Internet of Things (IIoT), machine learning and artificial intelligence (AI) for example are helping automate the process of analysing a growing number of datasets to understand and prognose machine health. Yet, most industrial anomaly detection efforts fail, with research from Capgemini showing almost 60% of organisations do not have the analytics capabilities to take advantage of the data generated from IoT sources. The issue is, many anomaly detection systems end up identifying either too many anomalies (false positives) or not enough (false negatives). Identifying true anomalies involves scouting for those “unknown unknowns”, amidst a sea of changing industrial data patterns. Avoiding downtime: Illuminating the dark spots in your industrial data The key is to detect early signals of future problems, and take proactive actions to prevent them. There are a few best practices used for anomaly detection and prediction that every manufacturer should look to follow: Rule-based/supervised vs […]