Detecting faults: A stitch in time for manufacturing?
By Alessandro Chimera TIBCO’s director of digitalisation strategy The way we use data to monitor and control manufacturing systems has changed. TIBCO’s director of digitalisation strategy, Alessandro Chimera, explains the shape of the anomaly economy guiding operational performance management in manufacturing. “A stitch in time saves nine” is a well-known and well-worn adage. It neatly encapsulates the reality that early remedial action to deal with a minor fault detected can avoid greater repair costs caused by damage down the track. Never has this saying rang truer than in today’s high speed, high volume, near real-time manufacturing processes, often described as Industry 4.0, where rapid production can make fault detection difficult. Without appropriate anomaly detection and rapid intervention, it would be easy for large volumes of a flawed product to be distributed before a problem is discovered, or for expensive manufacturing machinery to be seriously damaged. Detecting anomalies does not only increase production it can produce considerable savings: machine downtime and possible damage can be avoided along with product wastage and, most importantly, flawed products can be detected before they are delivered to customers. However, this is not an easy task, even with the availability of massive amounts of real-time data, artificial intelligence (AI) and machine learning (ML). This data is likely to originate in multiple different formats from different systems and scattered across many different locations. For example, some valuable data might need to be gathered from products that have already been shipped that have exhibited abnormal behaviour or failed. To make this data usable for AI and ML techniques, it must first be harmonised and related to the components of the manufacturing process it represents so it can be meaningfully analysed. This requires the use of an autoencoder. It is not an easy concept to explain in-depth, but simply […]