A history lesson isn’t necessary to know manufacturing has evolved over time. From the advent of mechanics to the electrification of factories for mass production to equipping production lines with robotics, the world of machines and processes is evolving before our eyes once again.
Manufacturing facilities are getting leaner. This isn’t by design. Baby Boomers who once dominated the landscape are now exiting the workforce in droves, so much so the industry is facing a deficit of 3.5 million workers. This has put a strain on organizations as they seek younger and less experienced personnel to do more with less. Since many manufacturers are operating with smaller staffs, equipment processes and manual checks are falling through the cracks.
Manufacturing is smaller and smarter
Plant floors are less staffed, but more connected than ever before. Thanks to the Internet of Things (IoT), data is available at our fingertips to harness and apply the information into predictive analytics to achieve higher levels of intelligence, orchestration and optimization. Logically, this led to condition monitoring.
A major component of predictive maintenance, condition monitoring presumes machinery will deteriorate and eventually break down. By being proactive and monitoring the performance of equipment through technology, data can provide the information to strategically schedule maintenance before an issue creates unexpected downtime. This prevents consequential damages and ensures the reliability of machines can remain high.
Effective condition monitoring establishes a condition monitoring culture
Condition monitoring utilizes various process parameters such as temperature, pressure, humidity, current, vibration and flows along with fluid media samples to monitor performance. Over time, these indicators of system and equipment health will become more predictable, reducing unscheduled downtime and increasing product integrity.
To achieve condition monitoring and a predictive maintenance program, it’s not enough to purchase test instruments and put them in the hands of untrained personnel. It’s imperative to let go of tried and tested methods and establish a new culture and approach of looking at maintenance. This means constantly developing, implementing, managing, measuring and improving condition monitoring. It requires commitment and full participation, otherwise, the vision is lost and the chances of a successful program decline. There are five things you need to know to ensure your condition monitoring program is prosperous.
Condition monitoring for predictive maintenance
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1. Choose the right machines
Which equipment are you going to monitor? You’re not going to pick random machines to evaluate. An Equipment Criticality Ranking (ECR) and/or Reliability Centered Maintenance (RCM) should be performed. An ECR identifies and addresses potential risks associated with the operation of the processing facilities. Failure scenarios are pinpointed, ranked and quantified in relation to the safeguards that protect against the scenario. The RCM focuses on avoiding the failure consequences, not the failure modes by ensuring systems continue to do what its users want in its present operating context. These are comprehensive lists of assets sorted in a ranked order and helps identify and determine which equipment should be tested on a regular basis. By performing ECR and/or RCM, organizations can develop unique maintenance schedules for each critical asset.
2. Select the right personnel
Choosing the appropriate personnel to be involved in predictive maintenance and condition monitoring is crucial. A common mistake organizations make is hastily assembling a team of their best mechanics rather than seeking the right technician who has the key attributes to master technology and performs investigative work. The selection of a condition monitoring team is handled in different ways from one organization to the next but should include individuals who demonstrate loyalty, intelligence and always pursuing training and self-development.
3. Implement condition monitoring training
Technicians involved in a predictive maintenance program receive little if any training beyond the information instructed by the vendor system. In fact, personnel seeks valuable training that directly impacts the effectiveness and success of the program. It’s crucial that all individuals are educated and can demonstrate the skillset to operate equipment, interpret the data, and report the information in a clear and concise way. A shortfall in this area will affect the quality of the overall initiative.
4. Be consistent and timely
Practice makes perfect. The same holds true for condition monitoring. There are a number of variables that can affect the accuracy of data. When it comes to testing equipment, collect data in the same location and on the same surface utilizing the same instruments to ensure consistency. Also, reviewing and interpreting information should be conducted in a timely manner. Otherwise, this will lead to unidentified equipment failures and unscheduled downtime.
5. Take action
You’ve inspected the equipment and collected the data, now what? It’s time to take action. Sounds simple enough, but there are many organizations who fail to take corrective action when machine anomalies are flagged. A predictive maintenance program receives the necessary support and funding to ensure success.
In today’s smart manufacturing world, condition monitoring is essential to determine machine health and implement the correct maintenance to ensure maximum performance and longevity. However, this cannot be achieved without having the right equipment, people, training and execution in place. Without a strategic plan, condition monitoring and predictive maintenance can become a wasted resource rather than a benefit component of your operation.
Learn more about condition monitoring strategies on your plant floor.
Article contributed by Dan Davis, product sales manager, SensoNODE™ Sensors and Voice of the Machine™ Software, Parker Hannifin Corporation.