Why It Matters
Over the decades, maintaining a competitive edge across asset-intensive industries required companies to adopt powerful efficiency tools and methodologies like Lean Six-Sigma and Total Quality Management. However, with the advancements in digital technologies and big data, many industry leaders are turning to ways to accelerate automation and data exchange to make better decisions.
Predictive Maintenance (PdM) is an essential part of many industrial companies’ 4.0 strategies. As a method of preventing unplanned downtime, by analyzing production and maintenance data, an organization can identify patterns and predict failure before it occurs. At the highest level of PdM maturity, companies are utilizing a combination of condition monitoring and process data for better detectability of hard to detect failure modes.
The “data silos” of condition monitoring data (e.g. vibration, ultrasound) and process data (e.g. flow, pressure, temperature) can be combined to obtain better detectability of hard to detect failure modes, which leads to earlier and better alarming. By embracing “the future of predictive maintenance” it’s possible to better support the decision-making process and predict previously unpredictable failures.