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Facility managers had long lived in a two-option trap (wait until the machine fails) (Reactive), or a calendar-based guess (Preventive). Both methods have flaws. Reactive maintenance causes expensive emergency repairs, whereas preventive maintenance usually causes wasting money in repairing machines that are not even damaged.
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Penetrate Predictive Maintenance (PdM).
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This guide will cover the definition of predictive maintenance, the way IoT and AI use it to predict failures, and the path to a strategy that can save you up to 50 percent of downtime.
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Predictive Maintenance (PdM) is a maintenance approach that is proactive and involves the use of sophisticated data analysis and condition-monitoring instruments to determine the health of equipment in real-time.
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In contrast to conventional methods, which are based on strict schedules or wait until a break has happened, PdM identifies the true state of in-service equipment to forecast when a piece of equipment needs the maintenance exactly. It changes maintenance as a schedule-driven estimation to a necessity that is condition based using three fundamental principles:
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• Real-Time Condition Monitoring: PdM is based on sensors that measure the real physical condition (vibration or heat) of the asset rather than assuming that the machine would be deteriorating because of age, condition, etc.
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• Just-in-Time Intervention: This is done to maintain the asset at the most appropriate time- that is, immediately before such an asset is likely to malfunction, yet not before that. This will remove the wastage of repairing machines that are running well.
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• Advanced Data Analytics: PdM can detect imperceptible patterns and anomalies unnoticed by human inspectors with the help of Internet of Things (IoT) sensors and Artificial Intelligence (AI), anticipating failures several days or weeks before their occurrence.
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We need to consider the position of PdM on the maturity scale of asset management to know it's worth.
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1. Reactive Maintenance (Run-to-Failure)

This is the approach to fixing when broken.
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• The Trigger: Malfunctioning equipment.
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• The Reality: It does not need any planning at all, but it is the costliest strategy. Unplanned downtimes are estimated at about 50,000 dollars per hour because of missed manufacturing. It introduces risks to safety and compels teams to pay premiums on rush components and overtime workforce.
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2. Preventive Maintenance (PM)

It is the "fix it, it is Tuesday, approach.
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• The Trigger: Minutes or use meters.
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• The Reality: This will prolong the life of the asset but will add to Over-Maintenance. Technicians frequently change parts on healthy machines just because that is what the schedule dictates, and this results in the squandered labor and budget. More so, failures may occur between the scheduled checks.
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3. Predictive Maintenance (PdM)

It is the approach to fixing it because the data says so.
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• The Trigger: Real-time information of an anomaly.
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• The Reality: PdM creates the golden meaning. It offers 8 to 12 days of early notice of failures, and it enables the teams to schedule the repairs at non-critical periods. It reduces chances of excessive maintenance as well as near eradication of unplanned breakdown.
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Predictive maintenance is not magic; it is a cycle of data collection, analysis, and doing that goes on and on. Here is the workflow:
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1. Data Collection (The Senses)

Equipment is monitored by sensors on critical assets. Common techniques include:
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• Vibration Analysis: It is used to identify misalignment or loose bearings in rotating machines.
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• Infrared Thermography: Hotspots Friction or electrical resistance.
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• Acoustic Monitoring: It involves the use of ultrasonic sensors to listen to leakages or frictions that would be invisible to the human ear.
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• Oil Analysis: Investigations of oil in the engine either metal shavings or water to determine internal wear.
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2. Analysis and Prediction (The Brain)

The information is sent to the cloud where it is analyzed by Artificial Intelligence (AI) and Machine Learning (ML).
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• Baseline Establishment: This is a system that gets to know what normal is like on your machine.
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• Anomaly Detection: These algorithms identify hidden deviations such as a slight temperature increase that a human being can miss.
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• Predictive Modeling: Advanced models determine the Remaining Useful Life (RUL) of a component and give 85-90 percentage accuracy in predicting failure.
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3. Intervention (The Action)

The system warns the maintenance team when there is information that there could be a failure. This enables an intervention to be targeted where necessary.
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A change in strategy towards a data-driven approach creates a quantifiable ROI. According to the industry statistics, the following are the fundamental advantages:
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• Significant Cost Reductions: The overall maintenance costs will be cut by 30% to 50 percent, and downtime will be cut by 50 percent through the application of PdM.
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• Increased Asset Reliability: Mean Time Between Failures (MTBF) improves by 30 percent through the early detection of defects.
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• Optimized Productivity: The labor productivity may be enhanced up to 20 percent due to the fact that technicians will cease making the unnecessary routine inspection of healthy machines.
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• Enhanced Safety: This will increase your safety by preventing accidents that could happen and pose a threat to the workers.
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Whereas the IoT sensors report problems, Cryotos CMMS gives your team the authority to resolve them. Data collection is one thing, but Cryotos is the command center, which transforms raw data into action.
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Herein lies the elevation of your strategy by Cryotos:
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• Automated Work Orders: Immediately transforms identified anomalies to work orders and removes the delay between detection and dispatch.
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• Smarter AI with Historical Data: Stores full asset history to model AI, and the failure prediction becomes easier and more accurate overtime.
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• Just-in-Time Inventory: The stock levels are checked automatically in case of forecasted failure, leading to purchase requests so that the parts are received at the moments of their requirements.
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• Seamless Integration: Smoothers the transition between the high-tech state of condition monitoring and your workforce, so that the insights can result in a fix that is done.
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The deployment of predictive maintenance is a process. You cannot and should not attempt to track all assets overnight. Follow this roadmap:
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Step 1: Identify Critical Assets

Start with the "P-F Curve" logic. Concentrate on the assets which are important to production, and which are costly to fail. Do not spend money on costly sensors to place on cheap and readily substitutable lightbulbs.
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Step 2: Establish a Baseline

You have to understand what health is before you can foresee the occurrence of failure. Install your sensors and allow them to run some time to obtain historical data and define the operating parameters of baseline operations.
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Step 3: Integrate with CMMS

Make sure that your condition-monitoring software is communicating with your CMMS. The insights do not help when the insights are not transferred to the technician. The aim is not to spreadsheets, which should be automated.

Step 4: Pilot and Scale

Conduct a pilot program in one line or type of assets. Estimate the ROI- expect to find less down time and less labor used. After demonstrating the concept, apply the technology to the other key assets.
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Predictive maintenance will help you move your facility away to the reactive mode of firefighting to the proactive mode of successfulness and help in avoiding unexpected down times before the bottom line is hit. With its powerful system that can be found in one of the leading systems, you can unlock enormous savings in costs and be assured that your critical assets will be running at maximum reliability in the coming years.
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Tired of making guesses when your equipment will go bad? Learn how Cryotos CMMS can optimize your predictive maintenance strategy to automate work processes and get the most out of your assets.