A genuine ally in the digital transformation of industries, predictive maintenance enables optimized management of equipment and infrastructures. The aim is to intervene at the right time, by anticipating machine behavior. Analytical maintenance, driven in particular by artificial intelligence models, provides frameworks and tools for analyzing data in real time. So, how does it work? What are the advantages of adopting it? What technologies support it?
Predictive maintenance is an asset management strategy which aims to anticipate failures and plan maintenance operations before a piece of equipment fails. Unlike corrective maintenance, which takes place after equipment has failed, predictive maintenance relies on regular data collection, analysis and monitoring to identify early warning signs of potential problems, using alert thresholds and drift indicators based on measurements of equipment health: changes in vibration, temperature, machine pressure, etc.
Of course, it is essential to correlate these analyses with the maintenance history of the equipment and the organizational capacity of the maintenance or methods teams.
Market trends at a glance
Growing since 2010, the predictive maintenance market is now considered mature, and dominated by American companies. It is fragmented, with many global players: Data & AI technology providers, IT services for businesses, etc. However, there is a niche market dedicated to predictive maintenance for more specialized players offering turnkey business solutions. This market segment has grown considerably in recent years.
According to Research and Markets’ July 2022 report on the Predictive Maintenance market[i], forecast growth in the global market remains strong, with +31%/year expected over the period 2020 to 2030. The financial forecast is also high, rising from 4.45BN in 2020 to 64.25BN in 2027.
Six sectors of activity have a strong demand for predictive maintenance solutions: manufacturing, energy and utilities, aerospace and defense, logistics and transport, community/government services, and healthcare.
The Key Principles of Predictive Maintenance
Predictive maintenance is organized around four stages involving the data life of the equipment studied.
1. Data collection
The first step in predictive maintenance is to collect data on equipment. This data may come from built-in sensors, manual readings or automated monitoring systems. The types of data collected can be very varied, ranging from physical measurements (temperatures, vibrations, pressure levels, etc.) to operating measurements (operating hours, service level, number of parts produced, etc.).
2. Data analysis
Once the data has been collected, it is subjected to in-depth analysis to identify trends, anomalies and behavioral patterns. Data analysis algorithms, some using artificial intelligence, detect early warning signs of potential failures. To refine the accuracy and relevance of the algorithm, it is essential to combine data and knowledge about the equipment or system being monitored (e.g. maintenance history, technical database, fault tree, etc.).
3. Intervention planning
Based on the results of this data analysis, maintenance operations are proactively planned. This can include operations such as replacement of worn parts or lubrication, as well as anticipated corrective maintenance operations to resolve a potential cause of failure with no apparent symptoms.
4. Follow-up and adjustment
Predictive maintenance is not static. It requires continuous monitoring to check the effectiveness of actions taken and adjust maintenance plans accordingly. Real-time data monitoring enables informed decisions to be made to optimize maintenance operations.
The benefits of a predictive maintenance approach
Depending on the industrial context in which the tool is deployed, the objectives vary: anticipating heating or elevator breakdowns, reducing the energy consumption of a building or energy expenditure in production processes, achieving carbon neutrality, limiting waste, controlling demographic pressure… So we can say that anticipating the occurrence of a breakdown is not the real issue! Quite often, it’s the consequences of a breakdown or failure that have an impact on production, energy consumption, risks, service rendered or costs.
So to approach predictive maintenance, digital machine and equipment management platforms analyze with the aim of forecasting and recommending, as does our BLPredict solution.
The approach is based on 3 pillars:
- Understanding when, why and how breakdowns occur
- Anticipate: the consequences of a breakdown
- Recommend: action(s) to be taken (maintenance and/or equipment configuration and/or environmental action)
The adoption of predictive maintenance can therefore provide answers to the industrial challenges of (1) improving the competitiveness of products and services supplied, and (2) increasing productivity.
The benefits are numerous, including
- Reduced downtime, by identifying problems before they lead to major failures
- Extend equipment life, by preventing premature wear and tear, ensuring regular maintenance, and promoting better management of spare parts inventories.
- Improved safety: early detection of problems helps reduce risks to worker safety by preventing catastrophic failures.
- Reduce repair and operating costs by organizing qualified maintenance operations
Key technologies for Predictive Maintenance
Implementing predictive maintenance requires expertise in several fields, such as IT development, architecture and networks, data analysis, maintenance and equipment expertise, and understanding the user experience. However, manufacturers seeking digital transformation rarely have all these resources and/or skills in-house. What’s more, as they move towards digital transformation and asset optimization, they may face difficulties in implementing a strategy for correlating and analyzing data from different languages. Finally, the diversity of machines and associated communications protocols, coupled with the inability to process the mass of data generated by AI-enhanced IIoT devices, can constitute serious barriers to entry.
For this reason, the technologies used to support predictive maintenance generally involve several players/solutions in the value chain.
- Smart sensor manufacturers
They are used to monitor key equipment parameters in real time. They transmit data to monitoring and analysis systems for constant assessment of equipment status. This is the link for integrators and publishers of IoT solutions, such as our partners Synox, Wattsense and Adeunis. Each has its own specific technical features in terms of sensor categorization, data management capabilities and equipment deployment.
- The Internet of Things for the Industrial World (IIoT)
IIoT enables real-time data collection from multiple devices, facilitating large-scale asset condition monitoring and analysis.
- Data analysis and artificial intelligence
The use of data analysis and artificial intelligence enables the detection of trends and patterns invisible to the naked eye, improving the accuracy of predictive maintenance. Rapid data processing and analysis, production of indicators and dashboards, secure decision-making… This is the very essence of our innovative BLPredict platform, interoperable with any CMMS software!
Predictive maintenance is a key element of efficient equipment management: by anticipating failures and planning maintenance interventions, companies can improve the reliability of their equipment, reduce costs and minimize downtime. Thanks to technological advances such as IIoT and AI, predictive maintenance is becoming increasingly accessible and effective for companies of all sizes, contributing to more reliable and sustainable production.
Finally, let’s close this article with a case study:ALSTEF GROUP’s feedback on the implementation of a predictive maintenance strategy.
[i](July, 2022) Predictive Maintenance for Manufacturing Industry – Global Market Trajectory & Analytics, Research and Markets
