Predictive maintenance software uses data science and predictive analytics to estimate when a piece of equipment might fail so that corrective maintenance can be scheduled before the point of failure. The goal is to schedule maintenance at the most convenient and most cost-efficient moment, allowing equipment’s lifespan to be optimized to its fullest, but before the equipment has been compromised.
The underlying architecture of predictive maintenance solutions typically consists of data acquisition and storage, data transformation, condition monitoring, asset health evaluation, prognostics, decision support system, and a human interface layer.
Predictive maintenance technologies include non-destructive testing methods such as acoustic, corona detection, infrared, oil analysis, sound level measurements, vibration analysis, and thermal imaging predictive maintenance, which measure and gather operations and equipment real-time data via wireless sensor networks. Predictive maintenance solution providers utilize these measurements and predictive maintenance machine learning techniques, such as the classification approach or the regression approach, to identify equipment vulnerabilities.
The predictive maintenance process flow consists of the following steps:
Determine equipment and failure mode to be monitored.
- Establish frequency.
- Monitor condition.
- Issue report.
- Is there an abnormality? If no, return to Step 3. If yes, continue to Step 6.
- Create a work order.
- Plan work date.
- Ensure parts and labor are available.
- Perform repair.
The data acquisition and storage component of predictive maintenance solutions must be able to collect and log data from all pertinent equipment for analytical purposes. It is also necessary that the system function wirelessly, allowing workers to collect data at any time, regardless of where they are located.
Data transformation plays a major role in predictive maintenance solutions because raw input data must be scaled or calibrated before it can be analyzed. Typically, raw input data is either scaled or calibrated via regression analysis, providing the ability to create an unbroken chronological record of the asset’s health history.
Condition monitoring is done using sensors deployed at strategic locations on the equipment being monitored. The sensors detect abnormalities in the equipment’s performance, allowing the system to provide information or alerts when necessary.
Asset health evaluation typically relies on detector definition, condition analysis, and prognosis by condition monitoring. Instrumental variables analysis is also common in asset health evaluation systems. Decision support systems are used to monitor the status of the equipment being monitored. If problems are detected, they are added to a problem log where they can be viewed by technicians or administrators when needed. The human-machine interface layer typically consists of an interactive viewer that allows technicians to view reports on their work order tickets and interpret data for real-time or trending analysis purposes by utilizing statistical models or expert systems.
Predictive maintenance problems can occur when an equipment supplier, integrator or technician fails to schedule maintenance in a timely manner, resulting in the equipment being used beyond its useful life. As equipment ages, it will become more likely to break down. By using predictive maintenance software, customers can maximize their investments by eliminating preventable breakdowns of expensive equipment while increasing the duration of usage on equipment that requires regular repairs. By automating preventive maintenance schedules, businesses can reduce costs significantly over time by reducing the amount of downtime they need to take during scheduled maintenance visits. Additionally, predictive maintenance can decrease labor costs since labor costs are directly affected by time wasted during scheduled maintenance visits. Predictive maintenance software takes a holistic approach to equipment maintenance by analyzing several pieces of data from multiple sources. This approach allows for a more complete picture of the equipment’s condition. In today’s manufacturing environment, there is a major focus on implementing production lines as quickly as possible and at the lowest possible cost. In many cases, the machines involved in these production lines are unreliable and need to be repaired often. Unreliable production lines can cause delays in getting products out the door and thus can result in increased costs and lost revenues due to missed customer delivery dates and refund requests. The use of predictive analytics software can help optimize maintenance schedules and thus decrease downtime needed to correct these unreliable production lines.