Reactive maintenance in the industrial sector is not very effective because it intervenes too late. Predictive maintenance is better, recalls Giovanni Prinetti, Solution Marketing Manager of Allied Telesis.
Maintenance and repair operations (MRO) are among the most important activities of modern companies in the industrial sector. They allow you to operate in an agile way, with a impact significant on productivity and profits. In particular, an important role is played by the management and maintenance of asset physical, such as machines and tools used in production environments. For this reason, companies are always looking for new and advanced ways of managing them, to which they allocate significant investments.
Avoid downtime
Typically, the most widespread approach to machinery maintenance is the reactive one, which involves repair or replacement in the event of a breakdown. However, reactive maintenance is not very effective. It often involves production downtime as operations need to be interrupted for the replacement of a specific device. This is why most companies employ a template alternative for the main assets, defined as preventive maintenance, which involves the replacement or maintenance of machinery before their nominal end of life. By being proactive, you avoid disastrous downtime as you never reach the end of the asset’s life cycle.
An expanding market
It’s not necessarily the cheapest method, because it tends to reduce the life of the product. And this is why some companies opt for condition-based maintenance effective of a machine, before a failure occurs. One of the most popular is predictive maintenance, which uses predictions about the residual useful life and end of life of the asset. Thus scheduling interventions at the most appropriate time, avoiding machine downtime and optimizing the overall efficiency of the equipment.
However, its adoption is challenging, as there is no simple and unique way to predict and anticipate industrial asset failures. According to a report by The Insight Partners, “Predictive Maintenance Market Forecast to 2028,” its market is expected to reach $26.58 billion by 2028, up from $5.31 billion in 2021.
The advantages of predictive maintenance
In recent years, the push of Industry 4.0, with IoT and IIoT, has allowed companies to gather large amounts of digital data on their production systems, facilitating their implementation with tangible economic benefits. Predictive maintenance has become one of the most popular applications due to its proven return on investment. In addition to its wide applicability in almost all industrial sectors, including manufacturing, oil and gas, mining, smart buildings and others.
Estimate remaining useful life with predictive analytics
The development of predictive maintenance solutions leverages large volumes of digital data from sensors, production systems and business applications, which are processed by advanced analytical algorithms such as machine learning and artificial intelligence, to help assess the condition of equipment. You can apply algorithms of predictive analysis to data from various devices. Such as, for example, vibration sensors, temperature sensors, energy consumption sensors, acoustic sensors, ultrasound and thermal imaging devices. Further information that is exploited is that coming from company information systems (such as ERP), for example on production quality and machine oil analysis, so that they provide insight useful on the operation and conditions of the system.
The rise of Asset-as-a-Service
At the basis of predictive maintenance solutions are digital infrastructures and technologies such as high-speed networks, Big Data platforms and advanced analysis technologies. In addition to reducing downtime and increasing overall equipment efficiency, predictive maintenance enables a series of new maintenance business models based on the Asset-as-a-Service (AaaS) paradigm. This allows OEMs to invest in products based on their use, without upfront fixed costs. To do this, they leverage information about the condition and health of assets to plan maintenance, repair and field service operations. They also deliver visibility on the status of the assets and the maintenance operations that must be performed. In this context, it is also possible to implement Maintenance-as-a-Service (MaaS) models, which charge customers for providing maintenance and repair information.
The advantages of predictive maintenance according to Allied Telesis
In the future, MaaS and AaaS will offer more flexibility in planning maintenance and repair operations. Without the need to resort to expensive maintenance contracts.