Predictive maintenance is a type of maintenance that directly tracks an asset’s health, status, and performance in real time. Predictive maintenance is aimed at reducing costly, unexpected breakdowns and offers the manufacturer the opportunity to plan maintenance around their own production schedule.
Through a combination of real-time data collected through the industrial internet of things (IIoT), predictive maintenance continuously analyzes the condition of equipment during normal operations to reduce the likelihood of unexpected machine failure.
With predictive maintenance, organizations can monitor and test various indicators such as slow bearing speed, lubrication, or temperature. Using condition-based monitoring and IIoT technology, these tools detect abnormalities during normal operations and send real-time alerts to the machine owner that indicate a potential future failure. More specific types of predictive maintenance including:
Vibration analysis: This is a common type of predictive maintenance used inside manufacturing plants with rotating machinery. It can detect imbalance, misalignment, or loose parts of equipment.
Infrared analysis: Using temperature as an indicator, issues related to airflow, cooling and motor stress can be identified.
Sonic acoustical analysis: Sounds can be converted to an auditory or visual signal that can be heard or seen by a technician, indicating conditions such as worn or under-lubricated bearings in both low and high-rotating machinery.
Although often used interchangeably, there are significant differences between both predictive and preventive maintenance.• Preventive maintenance occurs at regular intervals based on the machine’s lifecycle, regardless of usage to ensure that no issues emerge • Predictive maintenance only occurs when it is required based on the machine insights provided by the IoT sensors so as they wear over time, manufacturers can proactively schedule maintenance and avoid a costly, unexpected breakdown.
Although both forms of proactive maintenance are aimed at preventing machine failure, there is a significant difference between ‘condition-based maintenance’ and ‘predictive maintenance’. Condition-based maintenance (CBM) uses sensors to collect real-time measurements from a piece of equipment about various conditions, such as temperature, pressure, or vibration. Service is then delivered only once the condition status demands it—i.e. when your machine has hit a specified threshold parameter level. While predictive maintenance is a type of condition-based maintenance, it uses the constant stream of IIoT sensor data on a much larger scale. Rather than only taking the condition status into account, predictive maintenance leverages big data methodology to predict machine degradation based on asset history and related data. Predictive maintenance allows technicians to catch potential issues even earlier, so service can be scheduled more efficiently. On the other hand, condition-based maintenance often runs the risk of multiple machines requiring service at the same time.
Predictive maintenance is a transformative application of the IIoT with tremendous advantages. Below, we explore five benefits that can serve as differentiators for your organization:
Predictive maintenance enables technicians to detect issues in advance and resolve problems before equipment failure can occur, so you can:
There is no need to disrupt worker productivity for an unexpected malfunction or breakdown. Predictive maintenance plans around workers’ schedules, and:
By anticipating machine maintenance, service departments can generate major cost-savings and increased ROI through:
Harnessing the power of IIoT data collected through your machine’s sensors, product designers can use this vital information to:
An unexpected breakdown or malfunction can lead to hazardous working conditions for your employees. By predicting when a malfunction may occur, you can ensure:
To reap the benefits of a predictive maintenance program, you must lay the groundwork, prioritize critical assets, and start small with high-value use cases that can be scaled up over time. Here’s how to begin your service transformation: