Predictive Maintenance

Maintenance professionals need the ability to identify potential equipment failures. They need to understand the evolving patterns of equipment failure and thereby prioritize maintenance tasks.

Zura has mastered leveraging the key data points – lifetime data, run-to-failure data, threshold data required for RUL prediction. We use the proportional hazard models and probability distributions of component failure times to estimate RUL from lifetime data. Using a database of run-to-failure data from similar components or different components showing similar behavior, we estimate RUL using similarity methods. In absence of run-to-failure data or lifetime data, the information on prescribed threshold values is monitored continuously to predict potential failures and estimate RUL. Our expertise in working with Machine learning (ML) solutions enables real-time monitoring and proactive decision-making. Our solutions are trained to learn relationships between variables and failure events, accurately forecasting equipment failure likelihood or RUL.

Detecting equipment failures before they happen can be challenging when unexpected breakdowns occur, and this difficulty arises due to various factors. Lack of proactive maintenance measures and insights related to scrapable equipment and/or parts can result in equipment breakdowns.

Zura leverages advanced monitoring and proactive maintenance techniques coupled with buffer time to predict and prevent breakdowns. Our advanced analytics solutions slice and dice historical transaction data, other relevant features to identify patterns associated with bad actor scenarios. Our Real-time monitoring solutions using predictive models help detect possible breakdowns early in the game. Our solution can analyze production data, sensor readings, and quality metrics to identify patterns associated with defects or suboptimal performance. By predicting potential defects early, maintenance teams can take corrective actions to reduce scrap.

Like the demands of every business, maintenance and reliability teams have the constant requirement to do accurate budget forecasts.

With limited visibility to external factors, changes in labor costs, unexpected equipment upgrades and uncertainty in failure predictions can result in deviations. Adopting rolling forecasts and allowing for budget flexibility ensures predictive maintenance adapts to real-time changes and maintains accurate financial planning.