Feature: industrial electronics Figure 1: Memory buffer overflow attacks
A growing number of manufacturers are adopting predictive maintenance strategies, which allow systems to be maintained before failure occurs and thus run longer without interruption
Predict to prevent There are a number of processes that real-time integration and analysis of data can benefit. One example is condition monitoring, which involves monitoring a certain parameter of machinery condition, such as vibration or temperature, in which a change could indicate the development of a fault. Condition monitoring can be used as part of a predictive
maintenance strategy by running the condition data through pre-built predictive algorithms that can estimate when the piece of equipment might fail. This means maintenance work can occur in an organised way before the failure occurs, either without the need to stop production or during planned downtime, which avoids the sudden and costly onset of unscheduled downtime. This contrasts with the traditional strategy of reactive
maintenance, where manufacturers wait for a piece of machinery to fail before doing maintenance. According to a recent report by Deloitte, adopting a predictive maintenance strategy can reduce breakdowns by 70% and increase equipment uptime by 20%. A growing number of manufacturers are adopting predictive
maintenance strategies, which allow systems to be maintained before failure occurs and thus run longer without interruption. Market research firm MarketsandMarkets estimates that the global predictive maintenance market size will grow from $4bn in 2020 to over $12bn by 2025. The growing need to reduce downtime is highlighted as a major driver for the forecasted growth.
In supporting role An edge-analytics strategy supports a predictive maintenance strategy, but it’s important that manufacturers choose a platform that is simple to integrate and operate. Crosser Edge Node soſtware acts as a real-time engine, and its unique architecture hides complexity from the user for easy operation. Te soſtware allows manufacturers to collect data from any source, such as sensors or PLCs, and build automated workflows to transform, analyse and act on it. Te data can be processed at the edge to enable actions based on advanced business rules or codes to be performed rapidly. Any downtime can be a costly reality, but it can be minimised
if manufacturers are kept informed on the real-time status of equipment and processes. Being aware of these conditions is crucial to predicting the potential causes of downtime. Simple maintenance tasks can be performed to prevent downtime, or if downtime must occur, it can be planned in the best way possible to minimise costs and prevent wasted time. Edge analytics allows the collection, integration and analysis of
real-time data, enabling automatic changes to production whilst providing manufacturers with important data to make informed decisions. Te instantaneous information provided by edge analytics can be used as part of a predictive maintenance strategy to reduce unscheduled downtime. Manufacturers who implement edge analytics will then be equipped with the necessary data to make informed decisions about equipment and production, to optimise processes and maintenance schedules.
www.electronicsworld.co.uk December 2021/January/2022 35
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