Predictive Maintenance

What is predictive maintenance?

Predictive maintenance is an asset maintenance strategy that monitors the performance and condition of the equipment when an anomaly is observed in the data during regular operation to avoid untimely failure or breakdown. It helps identify defects and deviation issues by integrating equipment with industrial IoT sensors and artificial intelligence (AI) to analyze data in a connected environment.

Predictive maintenance is mostly, but not limited to heavy asset industries like manufacturing, oil and gas, mining, etc., that use capital-intensive machines and equipment to carry out daily industrial operations. A predictive maintenance strategy along with predictive maintenance software can help leverage data and analytics and helps operation and maintenance departments know the status of the equipment.

Predictive maintenance aims to optimize maintenance resources and reduce the operational costs associated with untimely equipment breakdown. It is essential for an equipment's smooth operation, which will help with quality production and a healthy bottom line.

Types of predictive maintenance

There are mainly three types of predictive maintenance: vibration analysis, acoustic analysis, and infrared analysis.

• Vibration analysis: Vibration analysis tracks abnormal or irregular vibrations in components,  machines, or equipment that may fail. It is primarily implemented in heavy asset-based industries like manufacturing plants, mining, shipbuilding, etc. Computer program algorithms analyze data collected through vibration tests. Then, vibration analysts identify and correct impending anomalies like looseness, unbalance, misalignment, and lubrication issues in the component.
• Acoustic or sonic analysis: This method uses sound waves to identify the changes in frequency caused by the component malfunction. It is mostly used in rotating equipment, motors, etc., where the component emits sounds on malfunction. Acoustic analysis is done in an environment free from background noise, and the data collected is deeply analyzed to find patterns in the wavelength. Depending on the degree of malfunction, corrective measures are taken.    
• Infrared analysis: It is one of the most cost-effective methods of predictive maintenance where the temperature is taken into account to identify faults in the machine. It is mainly used for electronic assets where heat, undetected energy loss, and motor stress are investigated to find the source of the problem and correct it. Infrared data can be collected by integrating all equipment into one central system where the analysis can be done using the software. It is the easiest among other techniques, as it doesn’t involve physical contact with the equipment.

Benefits of using predictive maintenance

Predictive maintenance is only performed on machines when abnormalities are seen in data flow or when the machine signals the need for maintenance work. Below is the set of benefits of predictive maintenance:

• Optimize maintenance costs: Predictive maintenance techniques make the most of the allocated maintenance cost. It is only performed on selected equipment where a future malfunction is predicted by data and signals for a maintenance job.
• Eliminate sudden breakdowns: This approach reduces the chances of implementing reactive maintenance as all the malfunctions and breakdowns are predicted. Operation and maintenance managers use predictive algorithms on data to see what part of the machine needs care, when, and at what time.
• Enhance asset lifespan: Predictive maintenance approach in the organization helps enhance the lifespan of the equipment. The performance of equipment is closely monitored. Hence, it is less prone to breakdown and provides a good return on investment (ROI).

Impacts of using predictive maintenance

The following are the impacts of using predictive maintenance:

• Reduces chances of equipment failure: Predictive maintenance acts supremely over preventive maintenance and reactive maintenance, reducing the chances of equipment failure to a great extent. 
• Allows for best ROI: Manufacturers and business owners get the most value out of investing in predictive maintenance as it helps get the best ROI. 
• Cuts costs and saves time: IoT sensors and artificial intelligence helps extract valuable data and allows maintenance to be performed only when required, assisting facilities in cutting costs, saving time, and maximizing resource utilization. 
• Helps achieve a healthy bottom line: A healthy bottom line can be achieved by implementing a well-formulated predictive maintenance strategy.

Predictive maintenance best practices

A predictive maintenance program, if implemented correctly, can significantly lower the repairing cost and save time. To make predictive maintenance work, companies must follow these best practices:

• Data collection through IOT sensors: Predictive maintenance programs need a huge amount of data to be analyzed and put into robust predictive models to derive accurate solutions. The data source is the equipment in a facility where the program is implemented. It is necessary to use IoT sensors to collect temperature data, vibration data, oil analysis data, alarm data, etc., and check these sensors from time to time to avoid discrepancies in data collection methods.
• Personnel selection and training: Predictive maintenance has a very slow adoption in the industry due to its overall solution complexities. It requires a team of experts with different skill sets to operate the whole system. Regular training is also essential to stay updated with the technological changes and innovations in the system. It requires personnel with experience in equipment maintenance, data analytics, IoT specialist, and operation analyst with skills in different types of predictive maintenance techniques.
• Measuring program success: It is essential to measure the success or failure of the predictive maintenance program and adopt a continuous improvement mindset to make it work. A predictive maintenance program involves collecting data, identifying PdM metrics, identifying PdM goals, choosing the right skills, using correct models for analysis, measuring outcomes, and communicating outcomes to the target audience in the organization. Measuring each metric and process helps identify shortfalls and helps take corrective action in the next cycle.

Predictive maintenance vs. preventive maintenance

A predictive maintenance program is only scheduled based on asset conditions, while preventive maintenance is more time-specific and scheduled in different time intervals to prevent equipment malfunction. Predictive maintenance uses predictive models to investigate irregular or abnormal data from equipment and schedule maintenance to correct it. Preventive maintenance involves investigating equipment performance and condition such as cleaning, lubrication, adjustments, repairs, and regularly replacing parts with a monthly, quarterly, half-yearly, or annual checkup. Predictive maintenance and preventive maintenance help improve asset reliability and reduce the risk of failures. However, both are different from each other with respect to the time of implementation of each program.