Predictive maintenance is estimated to create between 260 and 460 billion dollars in economic value in the global manufacturing use case setting alone.
McKinsey
Heavy industries are plagued by unplanned downtime, which Continuity Central estimates, costs upwards of $100,000 per hour for large manufacturers in most sectors. As a result, an increasing number of heavy industries are adopting predictive maintenance, which uses equipment data to signal when maintenance is required.
Predictive maintenance (PdM) can be further improved by applying artificial intelligence, which learns from equipment and surrounding process data and predicts future failures often weeks advance. The AI continually learns from historical and real-time data how the specific equipment operates in different conditions and detects minute changes. Once a deviation is detected OPUS alerts maintenance engineers and operators so they can review and plan interventions or maintenance activities.
With AI, PdM Operators can increase equipment lifespan, increase equipment uptime and reduce maintenance costs and efforts.
OPUS can detect failures on new equipment, as well as ageing equipment. Without relying on AI model libraries means that the one solution can be used throughout an organisation to model all assets, regardless of model type or brand, so that predictive maintenance can be adopted at an enterprise level.
Condition-based maintenance is enhanced with predictive analytics which discovers connections in data earlier, which if analysed manually would take months to uncover. These AI insights can be rapidly produced by asset reliability engineers and maintenance engineers without any programming or coding knowledge. They then review the insights and make informed business decisions based on their operational knowledge of the assets, the process, and the data. In this approach the AI doesn’t replace these highly skilled individuals but enhances their decision-making ability.
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