Predictive equipment maintenance is one of the main applications of machine learning in industry and construction.
Despite the rapid growth in previous years, the market is far from saturation and more and more companies are planning to introduce this tool in their production and the growth rate will only increase.
In this article, we will discuss the advantages of machine learning-based equipment maintenance.
Schedule or predictively?
Scheduled maintenance leads to situations where some mechanisms do not live up to repair and fail, which leads to downtime. Others are served more often than acceptable. Various external conditions, uneven load, personnel errors, various raw materials. All this leads to various wear and tear of equipment units, which cannot be provided for by the equipment documentation. Physical observation and examination of equipment for various noises and vibrations require highly qualified personnel and are time-consuming, and in the case of a large distance of equipment, it is impossible.
Monitoring equipment and the environment using IoT sensors allows you to see the situation in real time and quickly respond to critical deviations of parameters. However, as we said earlier, There is so much data that we can process only a tiny fraction of it. Evolution did not prepare our brains for such amounts of information. Therefore, methods are needed that will automatically monitor data from dozens of nodes in real time, identify implicit correlations and patterns and recommend what to do further. All these issues are solved by predictive maintenance.
The data is collected into a database and processed using machine learning methods. Algorithms identify patterns that previously led to breakdowns or downtime and signal an impending accident. This offers a range of benefits, from lower costs to increased security.
What is Predictive maintenance for?
According to the PwC report, companies are motivated to implement Predictive maintenance as follows:
Most - 47% increase in uptime,
Next - cost reduction - 17%
longer equipment life - 16%
Improved safety - 11%
In one of our projects, we predicted breakdowns of construction equipment large manufacturer. By collecting telemetry data, as well as available data on the operating conditions of the machines, we were able to predict with high accuracy breakdowns in various machine nodes. This made it possible for operators to order spare parts in advance, predict repair times and reduce the risk of serious accidents. In comparison with the vendor's recommendations, which were issued in accordance with the operating time of equipment components, our model turned out to be more accurate by about 30%.
According to mckinsey's report, the introduction of predictive maintenance increased profitability by 4-10% due to higher availability and operational efficiency.
What is needed for implementation?
As with any augmented analytics, implementing predictive hardware maintenance requires collecting hardware performance data. Data on breakdowns and unscheduled equipment downtime are especially important. All data will need to be consolidated into one database, normalized, binarized, and other preparatory work should be carried out. The more heterogeneous data and the longer the history, the better. Therefore, it is necessary to start collecting data as early as possible, this will in any case be useful for other areas of work optimization, for example, reducing energy consumption or optimizing warehouse stocks.
Predictive breakdown models integrate well with MES and ERP systems, which gives even greater effect. According to the same McKinsey report, predictive maintenance of equipment, combined with other digitalization of production, can reduce costs by 15-30%.
Our team has extensive experience working with industrial enterprises and we will be happy to audit your enterprise to improve its efficiency and reduce costs.