6 reasons to perform predictive maintenance of your company's energy and utilities using an efficient system

Submitted by Aline Gonçalves on Wed, 09/11/2019 - 19:22

Did you know that by 2023, 14.2 trillion dollars will be invested worldwide in Industry 4.0 technologies? And the forecast is that this number will reach 39 billion dollars in Brazil? These data from the study Winning with the Industrial Internet of Things also show that as the transformation is applied to industrial maintenance, it will bring savings due to efficiency gains, error prevention, and strategic planning.


Along with its usual management strategies, industrial maintenance incorporates new ways of thinking in both technical and administrative terms since new market demands have exposed the limitations of current management systems. Until the 1960s it was common practice to use equipment until its principal function failed, that is, until it was completely inoperative.

This behavior has been replaced by preventive maintenance activities for critical equipment, which has effectively reduced the incidence of severe failures. However, preventive maintenance alone does not provide the necessary conditions for in-depth forecasting about component failures or how to avoid impacts to production. In addition, in an industrial environment the preventive process requires frequent inspections, which shut down equipment, production lines, and even entire plants, generating additional costs by reducing production capacity and increasing inventory levels.

This state of affairs is the starting point for predictive maintenance, which, combined with preventive maintenance, can predict faults, determine in advance the need to repair or replace specific parts, can eliminate unnecessary disassembly for inspection, reduce unplanned emergency work, and use the total useful life of each piece of equipment, increase reliability of operation, and consequently reduce maintenance costs.

With predictive maintenance, the ideal time to perform maintenance is determined from the combination of information collected from a piece of equipment and the normative measures designated for it or the system it is part of. The implementation of this type of maintenance starts by monitoring the condition of machines. This process is facilitated by extensive instrumentation installed in industrial plants, automation programs, databases, and rapid flow of industrial information.

The integration of equipment, systems, and people in the production chain gives predictive maintenance greater effectiveness for planned actions, and this is nothing less than the essence of Industry 4.0 and the revolutionary concept of the Internet of Things (IoT). By combining production, automation, and IT solutions it is possible to revolutionize industrial production processes primarily by means of intelligent sensors and analyses driven by artificial intelligence. This means that companies can plan maintenance more easily because they are supplied with a clear, real-time view of equipment integrity.

Now you'll see 6 reasons to perform predictive maintenance in your company, together with innovative IoT-based technology systems.


The concern for worker safety in the industrial environment makes predictive maintenance a necessity rather than an optional activity in the production process. This is because if equipment departs from normal operating conditions, with predictive maintenance any anomaly can be detected immediately. This leads to safer working conditions workers and can prevent serious accidents.


By adopting the predictive methodology, unplanned maintenance and emergency work is reduced. Consequently there is an increase in reliability of the equipment and production line since the interruptions necessary for maintenance can be determined in advance.

Operability guarantee

Predictive maintenance increases the mean time between failures (MTBF), making equipment available for more time, and maintenance can be performed at the most convenient time and in a planned manner, improving the predictability of maintenance team activities, reducing interruptions in production and additional costs from emergency purchases of spare parts.

Cost Reduction

Cost reduction can be seen from several angles, for example: since the replacement of parts of parts is reduced, it is also possible to reduce the inventory budget. Another point is the reduction of time invested in repair, which leads to a reduction of production losses. A study conducted by McKinsey in 2015 foresees a 10 to 40% reduction of maintenance costs by 2025, as well as the reduction of energy consumption by between 10 and 20% with the implementation of predictive maintenance together with Industry 4.0 technologies.

Increasing useful life

Predictive maintenance helps maintain the optimal operating conditions of a piece of equipment: by collecting and analyzing data it is possible to create procedures that maximize the useful life of each machine.

Long-term strategic planning

Starting from the analysis of data collected on the factory floor, it is possible to perform scenario simulations and long-term projections, increasing the chances of finding more competitive prices by making long-term resource purchases and finding future opportunities for investments and improvements in the production process.

According to the United States Department of Energy, monitoring variables relevant to predictive maintenance can reduce maintenance costs by up to 30%, stoppages by 75%, and downtime by 45%. In the energy and utilities area the reduction can be seen from the analysis of data from sensors connected directly to substations, water and sewage treatment systems, compressed air lines, and cooling systems. Temperature sensors can show the heating trend of motors, and level sensors assist in the analysis of coefficients that influence level variation, expansion, or contraction of fluids. Ultrasound sensors are used to detect pressure and vacuum leakage with any type of gas, and can also identify current leakage from electrical discharge and corona discharge. Vibration sensors can identify current leakage and grounding failure as well as monitor energy quality and identify rotor, stator, and air gap conditions. Regardless of the methodology used, the data received from sensors aid in analysis to prolong the useful life of equipment and predict the ideal moment to replace it with minimal impact on the production process.

Predictive maintenance is an essential part of the factory of the future, and even if the concept is not well known, by combining its benefits with advances in the Industrial Internet of Things (IIoT) and artificial intelligence (AI) predictive maintenance can lead to significant savings for customers and manufacturers of industrial equipment.

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Energy Management Analyst, Viridis

Energy Management Analyst at Viridis, working in Energy Management and Strategic Planning at Bayer. Trained in Electrical Engineering with emphasis on Control and Automation at the Federal University of Uberlândia. Participated in R&D projects for intelligent energy measurement at Enel and CEMIG, conducted innovation, artificial intelligence, robotics, and gamification projects for corporate environments. 

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