TECH & AI
components, such as bearings, compressors or pumps, rather than treating a machine as one indivisible whole, catching subtle changes earlier while cutting false alarms.
The next breakthrough is human Industrial conditions do not usually stay still. Product grades, raw materials and throughput shift often, so ABB trains its models against specific operating states, feeding in variables such as load, temperature and pressure rather than treating all production data as one dataset.
In refining, Anindya explains, different crude types place different demands on the same equipment, meaning a machine can appear abnormal under one condition and perfectly healthy under another. Teaching models to recognise that distinction is what makes the difference between insight and false alarm.
In the future, Anindya is certain that the barrier is not a technical one anymore, but an adoption problem. The tools already work, but organisations need to learn where they deliver the most value. That, he insists, does not mean removing people from the equation. The companies seeing the greatest returns are those pairing machine intelligence with decades of hardwon operational knowledge, using it to help engineers spot risk earlier and decide faster.
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