Thanks to IoT, maintenance managers can predict when a failure will occur in the equipment fleet. Prediction is only possible with the arrival of new technologies such as sensors, which monitor the performance and deterioration of equipment over time. In this way, maintenance managers can track the condition of equipment based on monitoring and inspection data. This maintenance strategy is called predictive maintenance.
In this sense, this type of maintenance is a strategy that uses data analysis techniques to detect anomalies in equipment operation and possible defects, so that they can be resolved before failure occurs.
The following are the most used predictive maintenance techniques.
Predictive maintenance gathers information about the condition of equipment, based on historical and real-time data, that allows you to anticipate failures and calculate when intervention is needed. There are several key elements to a successful predictive maintenance strategy, such as IoT, artificial intelligence, and other integrated systems that allow different systems and assets to link together to share/analyze data.
Sensors installed on the machines allow sending data about the status and performance of the equipment in real-time thanks to IoT technologies, which enable communication between the machines and the analysis systems. The data that these sensors must measure and collect is related to the techniques used, such as vibration control, temperature, pressure, noise level, among others.
The storage systems in the cloud (cloud computing) allow you to apply data mining and recompile/analyze a large amount of data using Big Data.
Predictive models use machine learning technologies to establish patterns and comparisons, perform predictive defect analysis, and schedule maintenance interventions before failures occur. One of the most differentiating elements of predictive maintenance is to build and apply algorithms that provide a prognosis.
The techniques aim to improve equipment reliability through technology and best practices to increase productivity.
The main advantage of this maintenance strategy is to act in a timely manner, which reduces downtime and increases asset availability. On the other hand, it optimizes the equipment’s lifetime to the maximum, since maintenance is scheduled according to needs, it avoids wasting stock and manpower on unnecessary maintenance operations.
Predictive maintenance aims to define the best time to perform equipment maintenance, so that the productivity and reliability of equipment are as high as possible, without unnecessary costs.
Thus, the use of IoT is critical to the success of a predictive maintenance strategy, as is the use of sensors and other predictive maintenance techniques previously mentioned (vibration analysis, oil analysis, thermographic analysis, among others).
Although there are some disadvantages to this type of maintenance, such as high costs, need for specialized skills, and limitations of some equipment, this strategy allows maintenance managers to perform interventions only when is necessary. Thus, helping companies reduce costs, save time, and maximize resources.
From the same point of view, predictive maintenance reduces the volume of emergency repairs and the waste mentioned above, without hindering the company’s activity. In other words, downtime is planned well in advance, which allows for better maintenance, and helps control the maintenance budget.
Valuekeep maintenance management software is an essential tool to help you create and manage a more efficient maintenance strategy for your company. The Valuekeep solution offers you a range of features, two mobile apps and the possibility of integration with any IoT system, to optimise and streamline the maintenance management of your assets and infrastructures.
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