Predictive Maintenance Management

Predictive Maintenance Management
Do You Really Need It?

Predictive Maintenance (PdM) is a condition-based maintenance program that monitors the condition and performance of equipment during normal operations. Assets are monitored with sensor devices that supply the data about the asset’s condition. The data is used to reduce failures and predict when the asset will require maintenance or replacement. Integrating predictive maintenance with CMMS gives users the ability to set specifications, import sensor readings, create graphs and automatically generate work orders.

Predictive Maintenance vs. Preventive Maintenance

Predictive Maintenance and Preventive Maintenance both have the same objective of performing maintenance before equipment breakdowns, but are slightly different.

Predictive Maintenance
•  Monitors the performance and condition of equipment during normal production operations.
•  Estimates the exact moment of a failure, and repairs can be scheduled when necessary.
•  Requires an additional investment in maintenance tools and systems.
•  Requires additional personnel training on how to use the equipment and how to interpret the data.

Preventive Maintenance
•  The maintenance is performed on predetermined assumptions, based on manufacturers’ recommendations or history.
•  A planned and scheduled maintenance routine is put in place to extend the life of assets and reduce downtime.

Implementing Predictive Maintenance

The key to implementing the ideal Predictive Maintenance program depends on the type of condition monitoring technology is going to be used and the organization’s strategy for achieving maintenance excellence. The basic steps include:

Determining the Need
•  You have a good dataset on your assets with a good insight of providing actionable results that can lead to business goals and objectives.
   –  Critical assets identified.
       * Assets that require extensive repairs and are costly to replace.
   –  Historic and repair data.
      * Predictive Maintenance readings usually depend on a set of algorithms and need to have historic data to build an accurate model.
   –  Root cause analysis on failures.
      * Performing a root cause analysis on past failures is important in setting a baseline.
      * Knowing what caused the failure is an important data point on predicting the next issue.
      * Predictive Maintenance algorithms need this data to determine the signs to trigger an issue.
   –  Subject Matter Experts, who can provide some insight on any asset.
      * Having someone who knows the patterns of success or failures on equipment is an advantage.
   –  Business Objective – such as; increase margins, reduce downtime, etc.
      * It is important to understand the business objectives in order to evaluate the resources needed to achieve those objectives.

Centralize Data
•  Compile all of the data into a centralized database, such as a CMMS.
   – CMMS provides many benefits in the managing, storing, receiving and analysis of data.

Establish Dataset Criteria
•  What types of data output do you need that meets your goals and objectives for using predictive maintenance?

Develop and Test Criteria
•  Test to make sure that the dataset criteria is providing you with valuable data to meet your goals and objectives.

Production
•  Once you have identified the criteria that meet your goals and objectives, apply the attributes to the rest of your assets.

Predictive Maintenance and CMMS

Having a good CMMS in place maintaining all of the information on assets, makes it a lot easier to set parameters and data points so that implementation of the PdM meets expectations.

•  CMMS stores and maintains the core data on assets, equipment, etc., which is the starting point for the PdM.
•  Work Orders, notifications are automatically generated when predefined parameters have fallen outside the designated range.
•  CMMS takes the PdM data and incorporates the information into the centralized database along and merges it with other data on the asset (such as; repairs, spare parts, images, etc.).
•  Predictive sensors provide the raw data, and CMMS pulls all of the existing data together to provide a more in-depth analysis of the situation.

Types of Condition Monitoring Equipment

There are all types of sensors that measure different kinds of specifications. Some of the more common sensors measure vibration, noise, temperature, pressure, and oil levels.
•  Vibration detects vibrations outside normal conditions.
•  Thermal Imaging measures temperature internally or via infrared.
  Pressure records liquid or air pressure over time.
•  Electricity measures current over time in assets.
•  Sonic an Ultrasonic identifies high-frequency sound in machines.
•  Water Presence detects moisture and leaks in air conditioning units or pipes.
  Oil Analysis checks the condition of oil to see if other particles and liquids are present. This analysis also identifies any leaks and shows what parts of the machine are wearing out and by how much.

Incorporating Predictive Maintenance monitoring into a CMMS provides additional tools to help analyze results from these devices and sensors to accurately make business decisions.

Predictive Maintenance & Additional Expenses

Depending on your organization’s needs, implementing a Predictive Maintenance Programs requires:

•  Additional purchase of hardware to monitor assets
•  Additional employee training on the equipment
•  Additional employee training to interpret and analyze data

These expenses are usually not provided by software vendors.

Conclusion

To implement an effective Predictive Maintenance program you need the right tools. Integrating Predictive Maintenance into a CMMS platform is the first step to implementing an effective Predictive Maintenance program.

Predictive Maintenance can be used across any industry and any size company. Although Predictive Maintenance provides more accurate information and controls when maintenance needs to be done, it is time-consuming to setup and is costly. With the additional expense of purchasing monitoring equipment and the need of highly qualified technicians to accurately interpret the data, for the short-term this can be a very expensive solution. Over a period of time, organizations may see this as a cost-effective strategy, reducing total time and cost spent on equipment maintenance.

For those organizations that can’t afford the expense of implementing a Predictive Maintenance program, a CMMS Preventive Maintenance solution is a great affordable alternative and packed with features for managing asset life cycle, maintenance schedules work orders, costs, etc. A CMMS is easy to use and does not require additional personnel to manage the system, does not need the purchase of additional hardware or software. Implementation is quick and easy.

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