Predictive maintenance (PdM) anticipates maintenance needs to avoid costs associated with unscheduled downtime. By connecting to devices and monitoring the data they generate, you can identify patterns that lead to potential problems or failures. Predictive maintenance is a proactive, data-based approach to the maintenance of industrial equipment. By using a network of connected sensors to monitor in-service equipment, operational data is collected, transmitted and analyzed.
Predictive maintenance programs use the results of this analysis to automatically generate service alerts when equipment is operating in suboptimal conditions or if it will soon require routine maintenance based on common usage patterns. When you consider the growth and productivity of organizations in different areas, you don't have to wait long to see a pattern that allows you to establish the maintenance strategies used in all operations that are constantly thriving. Consider the case of the Industrial Internet of Things (IIoT), which is a network of devices and sensors that collect and share data to allow companies to predict when equipment will fail, schedule preventive maintenance and avoid unforeseen downtime. The development, management, and governance of machine learning models are critical to the success of this type of maintenance.
In general terms, a maintenance manager and maintenance team use predictive maintenance tools and asset management systems to monitor impending equipment failures and maintenance tasks. Each manufacturing area has its specific and preferred types of maintenance, depending on the processes and machines used. Preventive maintenance is carried out at a time before the fault and is cyclical in nature, since the exact moment when the adverse effect will occur is unknown. Corrective maintenance makes it possible to make the most of the equipment, but at the same time it involves a lack of reliability, since the exact time of the failure is unknown and this could cause safety problems.
As part of this stage, it is possible to detect anomalies, train a classifier to identify different types of faults, obtain information about what part of the machine, equipment or component needs to be repaired, and predict the trend that the machine is likely to follow on its path to the transition between states. Augmented reality in industrial maintenance reduces the number of untimely revisions and applies the necessary care when and where it is most needed to maintain operational continuity. Leverage the cloud to work better together in the new connected era of maintenance and asset management. Other potential problems that preventive maintenance could involve are related to the fact that, depending on the environment in which it operates, each machine works differently and the frequency of necessary maintenance may change.
The state-based approach to maintenance is expected to increase due to the immense need to keep industrial machinery in good condition. It differs from preventive maintenance in that the corrective maintenance task is not planned in advance, but rather arises because of a fault or problem detected in the system. As an integral part of Industry 4.0, predictive maintenance technology uses data to drive increased efficiency and profitability.