Understanding Condition Monitoring
Condition monitoring is the process of determining the condition of machinery in real-time by monitoring certain parameters like vibration, temperature, or sound and measuring them against fixed thresholds. A significant change in any parameter may be indicative of a machine performance deviation. Understanding the condition of machines/equipment in real-time enables organizations to optimally schedule maintenance tasks and run preventive measures to avoid and minimize damage and/or downtime.
Conventionally, condition monitoring is performed by installing measuring equipment – detection sensors that send real-time data to a monitoring software (a computerized maintenance management system or CMMS). This software compares the value of the measured parameter against preset baselines to determine if the monitored machinery is working optimally or not.
Condition monitoring is vital to reducing, and in many cases preventing, downtime in factories and industries that rely on heavy machinery, especially where the machine or equipment is a critical process component.
A 2018 survey report by Aberdeen Research claimed that 82% of the companies surveyed experienced unplanned downtimes that lasted an average of four hours and cost an average of $2 million over a three-year period. The purpose of condition monitoring is to try and avoid such downtime by catching performance deviations, especially abnormal ones, in faulty machinery early enough to take rectifying measures.
Condition monitoring is thus a necessary implementation for any industry with simple to complex machinery. So, why is it being replaced by predictive maintenance?
Why Predictive Maintenance Is an Upgrade
For starters, condition monitoring is not so much being ‘replaced’ as it is being ‘upgraded’. To fully realize this, one must understand predictive maintenance.
Predictive maintenance is the process of gathering and analyzing data pertaining to the condition of machinery to predict when a breakdown could occur or when maintenance should be performed.
The keyword here that differentiates predictive maintenance from condition monitoring is timing. Predictive maintenance analyzes machinery parameters to predict when they will deviate from standard rather than wait for deviation to be measured before raising an alarm, as is the case with condition monitoring.
Condition monitoring alerts you of an issue in real-time after a parameter has deviated from the standard (the deviation, albeit not critical, allows the maintenance team to take action before there is downtime). Predictive maintenance alerts you of a potential issue by predicting that certain parameters will deviate from standard even before they do by spotting anomalies in patterns.
Condition monitoring helps you analyze the symptoms to alleviate the disease. Predictive maintenance helps you prevent the disease even before symptoms arise by predicting when these symptoms are most likely to occur.
The timing is the advantage predictive maintenance has over condition monitoring. Predictive maintenance enables companies to service and maintain machinery ahead of time (or just in time, rather) to prevent any failure or downtime.
Predictive maintenance uses both sensors similar to condition monitoring (but more precise, for example, piezo-electric transducers) and data analysis (forecasting through machine learning and explainable AI) to convey results.
The Advent of Industry 4.0 and, with It, Predictive Maintenance
The Fourth Industrial Revolution will take the industrial digitization that was started in the third revolution and enhance it with interconnected, autonomous, and smart systems powered by machine learning. Industry 4.0 brings to the table, most specifically, the Internet of Things (IoT).
Predictive maintenance is possible because of two main factors – more accurate sensors (seismic or piezo-electric transducers, infrared thermography, ultrasound sensors, etc.) and the predictive analysis (powered by machine learning) of data collected through sensors and devices connected via IoT.
How Predictive Maintenance Works
- Setting the Baseline for Measurement: The maintenance team defines the thresholds for the acceptable operation of assets.
- Installing Interconnected Sensors (IoT Devices): High-sensitivity sensors that will send real-time data for condition monitoring are installed for assets to be monitored. These IoT devices are interconnected to each other and the CMMS (computerized maintenance management system).
- Data Processing and Analysis: The CMMS, enabled with machine learning, can analyze collected data, study patterns, and predict potential issues with machinery and equipment.
- Manual or Automated Maintenance Scheduling: Maintenance decisions can be taken either manually or automated into the CMMS. Automated maintenance tasks are triggered as soon as an issue is predicted, creating a seamless and automated maintenance cycle.
Is Predictive Maintenance Worth It?
The O&M Best Practices Guide by the US Department of Energy states that “a well-orchestrated predictive maintenance program will all but eliminate catastrophic equipment failures.”
The report also indicated that cost savings compared to preventive maintenance are 8-12% higher and compared to reactive maintenance are 30-40% higher. Here are some other interesting stats that clearly prove the ROI on predictive maintenance is positive (below figures are averages from respondents surveyed):
- Return on investment: 10x
- Reduction in maintenance costs: 25-30%
- Elimination of breakdowns: 70-75%
- Reduction in downtime: 35-45%
- Increase in production: 20-25%
It is evident that maintenance activities are critical for any industry that relies on machinery, where the downtime of machinery leads to the downtime of the entire process chain. Predictive maintenance enables industries to predict potential issues in order to perform proactive maintenance, and the ROI is clearly huge.
Make the Most of Your Assets With Eugenie
Eugenie is designed to provide a complete overview of all operational assets within your facility. In comparison to service-heavy alternatives, Eugenie is easy to deploy and use.
Real-time monitoring and AI-powered predictive insights enable industries to boost their operational efficiency and reliability and achieve increased productivity with the same set of assets.
Here’s what Eugenie offers industries in the manufacturing, oil and gas, energy, mining and minerals, and smart city sectors:
- Predictive analytics to reduce unscheduled downtime.
- Actionable recommendations to delay or avoid predicted failures.
- Machine-level GHG emissions data.
- Root cause with evidences to transparently show what triggered the failure predictions and actionable recommendations.
- On-time maintenance to ensure reliable asset operations and improved output quality.
- Optimized operations to decrease industrial waste.
Some of Eugenie’s core features include:
- Spot: The system detects anomalies in high-volume industrial data, giving you advanced warnings for potential issues.
- Explore: Infer, from analytics, what caused the deviation (root cause analysis) from its standard.
- Exploit: Simulate scenarios to predict machine and equipment conditions.
Arm your decision-makers with AI-powered data-driven solutions like root-cause analysis and predictive insights to optimize asset life and usage. Eugenie harnesses the power of Explainable AI to enable you to get the best out of your assets.
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To learn more about how Eugenie has helped companies across the globe ensure a profitable transition to operational sustainability, talk to us today or feel free to reach out to us at firstname.lastname@example.org.