Turning Sensor Data into Maintenance Action
Collecting sensor data is easy. Turning it into maintenance action is where most teams struggle.
Condition monitoring only delivers value when vibration, temperature, and run-time data are used to guide real maintenance decisions—not just populate dashboards. By focusing on trends, integrating data into existing CMMS workflows, and starting with critical assets, maintenance teams can move from reactive repairs to planned, reliability-driven work. This practical approach helps reduce downtime, eliminate unnecessary PMs, and improve asset performance without overhauling the entire maintenance program.
The Top Maintenance Metrics That Drive Better Decisions
Maintenance teams have more data than ever—but more data doesn’t always mean better decisions. When dashboards are overloaded and metrics aren’t tied to action, valuable insight gets lost in the noise.
The most effective maintenance organizations focus on a small set of meaningful metrics that directly support reliability, risk reduction, and business outcomes. By combining core maintenance KPIs—like unplanned downtime, asset criticality, and cost by asset—with condition monitoring trends such as vibration and temperature, teams can prioritize the right work at the right time. This data-driven approach aligns with the SMRP Body of Knowledge and helps shift maintenance from reactive task completion to proactive, risk-based decision-making.
Why Data-Driven Maintenance Is Replacing Time-Based PMs
For decades, time-based preventive maintenance has been the default approach—servicing equipment on a fixed schedule, regardless of its actual condition. But across industries, maintenance teams are realizing that the calendar isn’t the best indicator of risk. Most failures aren’t age-based, and time-driven PMs often lead to unnecessary work while still allowing critical issues to slip through.
Data-driven maintenance shifts the focus from “Is it time?” to “What is the equipment telling us?” By using condition monitoring data like vibration and temperature—alongside asset criticality and failure history—organizations can make smarter, reliability-focused decisions. This approach aligns with proven RCM principles and the SMRP Body of Knowledge, helping teams reduce wasted effort, catch failures earlier, and improve uptime where it matters most.
Turning Sensor Data into Action: Making Predictive Maintenance Work for Your Team
Collecting sensor data is easy—acting on it is the challenge. Learn how maintenance teams can turn vibration, temperature, and lubrication data into actionable insights that reduce downtime, improve reliability, and make predictive maintenance work in the real world.
5 Maintenance Mistakes That Are Hurting Your Reliability—and How Predictive Sensors Solve Them
Even the most experienced maintenance teams make costly mistakes—from over-servicing healthy assets to reacting too late to early warning signs. This post breaks down five common maintenance pitfalls and shows how predictive maintenance with condition monitoring sensors helps teams reduce downtime, cut costs, and improve equipment reliability.
Condition monitoring techniques
Since the dawn of industrialization, Condition Monitoring Techniques have evolved into an indispensable practice, performed at plants to safeguard the reliability and efficiency of industrial machinery, embracing technological advancements along the way. The methods used changed from visual and qualitative inspections to using hand-held devices to quantify the data — to today, where technology is available to monitor the condition of an asset anywhere in the world on a smartphone.