How predictive maintenance supports sectors across the globe
New technologies, enhanced with artificial intelligence (AI), and years of strategic testing have enabled predictive maintenance solutions to transform how sectors across the globe operate.
Shifting the focus from reactive to proactive, predictive maintenance uses data from specialist sensors, machine learning, AI models, and condition monitoring to pinpoint potential problems with equipment and machinery. By using these predictive maintenance techniques, sensors connected to the Internet of Things (IoT) constantly monitor for changes in vibrations, sound, temperature, and outputs with precision, allowing operators to fix issues before they become a wider threat to production.
The technology used is not sector-specific; the predictive maintenance systems and sensors have instead been developed to suit a variety of applications. Similar predictive maintenance solutions and techniques are used across multiple industries simultaneously. For example, companies like Purple Sector have taken their experience monitoring Formula 1 racing vehicles and developed advanced predictive maintenance sensors. These Purple Sector sensors are then used within the manufacturing industry to track the performance of equipment and production lines.
Before data-driven and AI-based predictive maintenance solutions were introduced, organisations and businesses with large assets, like machinery, multiple vehicles, and production lines, relied on traditional maintenance methods. Companies with assets like these simply used preventive and reactive methods, with maintenance schedules set up manually according to the equipment manufacturer’s guidelines. Regular inspections and services were carried out by technicians on equipment, machinery and production lines during pre-planned downtime. These fixed schedules often saw parts replaced too soon, wasting operators’ time and money, or too late, with parts failing unexpectedly and equipment running continuously until it broke down. Using data-driven predictive maintenance methods instead helps avoid unexpected downtime, lowers repair costs, and can extend the lifespan of equipment and machinery.
Sectors using predictive maintenance solutions
Transportation and logistics
The growth of rapid global shipping means that railways, airlines and large transportation companies now rely on predictive maintenance solutions to ensure their fleet is working efficiently. Some of the most advanced predictive maintenance technologies have been developed using knowledge and data sourced from within the logistics sector. IoT-connected sensors are used to monitor critical performance and safety variables in the engines of planes, trains and heavy goods vehicles, allowing operators insights into when maintenance is required. The transportation and logistics sector is constantly working around the clock, so having predictive maintenance systems flag issues before they escalate stops potentially catastrophic incidents from occurring. Predictive maintenance technologies used within this sector have also helped to reduce service interruption on train lines, improve shipping reliability, and enable vehicle maintenance teams to work efficiently. Having sensors on equipment and vehicles that are deployed across the globe enables organisations to flag issues remotely, so operators can stay in control and fix their equipment as required.
Manufacturing and production
The manufacturing sector relies heavily on predictive maintenance solutions to monitor production lines and equipment. With previous maintenance methods costing businesses money during lengthy periods of downtime, new AI and data-driven solutions are helping to cut costs by preventing equipment failures and speeding up production processes. By monitoring equipment in a factory or manufacturing facility with sensors designed to pick up any signs of change or potential failure, operators can see how their production line is functioning in real-time. Using the data sourced from the sensors allows manufacturers to know when to step in to prevent any potential issues, as well as implement changes to improve production while the process is ongoing. AI-driven predictive maintenance solutions have also allowed traditional manufacturing facilities to become smart factories, with integrated technology used throughout to boost production.
Facilities management
Commercial property owners and those responsible for overseeing large buildings and facilities such as offices, shopping malls, hospitals, and apartment blocks often rely on predictive maintenance solutions for support. The property owners and teams responsible for maintaining these buildings use predictive maintenance tools to monitor critical assets throughout the facility, alerting them to any potential failures or changes. This allows maintenance teams to step in and fix any broken or damaged equipment before it becomes an issue for users, residents or staff.
Modern buildings can be equipped with systems of integrated predictive maintenance tools, AI-driven technology, and IoT sensors that monitor internal infrastructure and alert maintenance teams to any potential problems. The sensors and predictive maintenance solutions can connect to heating and cooling systems, elevators and escalators, automatic doors, and electrical systems throughout the building to collect data in real-time. These insights allow property managers to accurately identify areas of improvement and spot specific equipment that requires maintenance.
Having automated predictive maintenance tools integrated within a facility sends alerts to technicians about what is going wrong, so they can step in before staff or residents in the building even know there was a problem. These automatic alerts help facilities managers avoid costly repairs from facilities breaking down completely, as the predictive maintenance sensors detect minute changes within equipment that can signal potential problems. Facilities teams that use predictive maintenance systems within their buildings to make improvements can prevent incidents from happening that cause complaints from clients, customers and residents over damaged or out-of-use facilities. Using predictive maintenance solutions for facilities management offers a nicer experience for users while extending the lifespan of assets and the property overall.
Energy companies and utility providers
Predictive maintenance technology enables the energy and utilities sector to be more proactive and connected than ever before. Energy companies and utility providers have been using predictive maintenance solutions to simplify processes and monitor equipment for decades, with advancements allowing them to expand services reliably and globally. Predictive maintenance systems are essential given the critical nature of the products, assets, and infrastructure in the industry, as well as the regulatory standards they must uphold. Any unplanned outages can cause widespread chaos and disruption for clients and customers if they are left without electricity, heating, gas, or water. These outages also come at a cost to the energy and utility companies, with costs rising the longer the outage lasts, so having methods in place to prevent them from happening is imperative. Power plants, energy companies and utility providers use predictive maintenance tools and IoT-connected sensors to monitor all of their most critical assets, including wind turbines and offshore wind farms, oil rigs, electricity grids, as well as internal transformers and transmission infrastructure. With predictive maintenance methods in place, utility companies can prevent major outages as the systems are designed to analyse data to spot potential problems and signs of equipment failure. Automated alerts tell maintenance teams when something changes within the widespread web of critical infrastructure, pinpointing the specific issue and location, allowing technicians to quickly step in and fix the equipment.
Having widespread connectivity with predictive maintenance systems in place prevents costly downtime from occurring by allowing maintenance teams to repair damaged infrastructure before it becomes a large-scale issue. Predictive maintenance helps large companies across a range of sectors remain in control of their assets and infrastructure, enabling them to work globally, save money, and continue providing reliable products and services to clients and customers.
