Predictive maintenance (PdM) builds on condition-based monitoring to optimize the performance and lifespan of equipment by continually assessing its health in real time. By collecting data from sensors and applying advanced analytical tools and processes such as machine learning (ML), predictive maintenance can identify, detect and address issues as they occur, as well as predict the potential future state of equipment, and so reduce risk. The key is providing the right information at the right time to the right people.
Maintenance strategies and maturity depend on factors such as asset/replacement cost, criticality of asset, usage patterns and impact of failure on safety, environment, operations, finance and public image. Predictive maintenance is one of three leading maintenance strategies used by businesses, the others being reactive maintenance which fixes failures when they occur, and preventive maintenance which relies on a predefined maintenance schedule to identify faults. Because predictive maintenance is proactive it enhances preventive maintenance by providing continuous insights on the actual condition of the equipment rather than relying on the expected condition of the equipment based on a historical baseline. With predictive maintenance corrective maintenance is only carried out only when there is a need to do so, and so avoids incurring unnecessary maintenance costs and machine downtime. Predictive maintenance uses time series historical and failure data to predict the future potential health of equipment and so anticipate problems in advance. This enables businesses to optimize maintenance scheduling and improve reliability.
Predictive maintenance also differs from preventive maintenance in the diversity and breadth of real-time data used in monitoring the equipment. Various condition monitoring techniques such as sound (ultrasonic acoustics), temperature (thermal), lubrication (oil, fluids) and vibration analysis can identify anomalies and provide advance warnings of potential problems. A rising temperature in a component, for example, could indicate airflow blockages or wear and tear; unusual vibrations could indicate misalignment of moving parts; changes in sound can provide early warnings of defects that can’t be picked up by the human ear.
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Predictive maintenance relies on a variety of technologies including the Internet of Things (IoT), predictive analytics and artificial intelligence (AI). Connected sensors gather data from assets such as machinery and equipment. This is collected at the edge or in the cloud in an AI-enabled enterprise asset management (EAM) or computerised maintenance management system (CMMS). AI and machine learning are used to analyse the data in real time to build a picture of the current condition of the equipment, triggering an alert if any potential defect is identified and delivering it to the maintenance team.
As well as providing defect warnings, advances in machine learning algorithms enable predictive maintenance solutions to make predictions about the future condition of equipment. These can be used to drive greater efficiency in maintenance-related workflows and processes such as just-in-time work order scheduling and labor and parts supply chains. Furthermore, the more data collected the more insights are generated and the better the predictions become. This gives businesses the confidence that equipment is working optimally.
Benefits from a predictive maintenance strategy centre around anticipating equipment faults and failures, reducing maintenance and operating costs by optimizing time and resources, and improving the performance and reliability of equipment. Deloitte reported in 2022 that PdM can result in a 5-15% reduction in facility downtime and a 5-20% increase in labor productivity.1 Predictive maintenance also has a beneficial impact on operational sustainability by minimizing energy usage and waste.
Optimizing asset performance and uptime can reduce costs. Advance warning of potential faults will result in fewer breakdowns as well as reduced planned maintenance or unplanned downtime. Greater continuous condition visibility will enhance the lifetime reliability and durability of equipment. The use of AI can more accurately forecast future operations. This latter benefit is paramount in a world where rising prices and unpredictable events like the pandemic and climate-related natural disasters have exposed the need for more predictable spare parts inventory and labor costs and a lower environmental impact from operations.
Productivity can be increased by reducing inefficient maintenance operations, enabling a faster response to problems via intelligent workflows and automation, and equipping technicians, data scientists and employees across the value chain with better data with which to make decisions. The upshot is improved metrics such as mean time between failures (MTBF) and mean time to repair (MTTR), safer working conditions for employees, and revenue and profitability gains.
There are barriers to predictive maintenance which can be costly, at least in the first instance.
Assessing the criticality and cost of failure of individual assets also takes time and money but is fundamental in determining if predictive maintenance is appropriate — low cost assets with cheap readily available parts may be better served with other maintenance strategies. Predictive maintenance programs are hard but the competitive and financial advantages of a well executed strategy are significant.
Predictive maintenance technologies are already being adopted across industries for many assets whether that be cash points, wind turbines, heat exchangers or manufacturing robots. Asset-intensive industries such as Energy, Manufacturing, Telecommunications and Transportation, where unforeseen equipment failures could have widespread consequences, are increasingly turning to advanced technologies to improve equipment reliability and labor force productivity. Potential uses are many and varied:
Power outages (PDF) can cost energy companies millions of dollars in compensation and can lead to customers switching providers.
Equipment failures and unplanned downtime can significantly increase unit costs and create supply chain disruptions.
Fixing telecom network errors quickly is critical in improving the quality of services — even small network outages can impact huge numbers of customers.
Identifying points or brakes failures or track deformations will prevent service interruptions and ensure passenger safety.
The ability to better assess structural integrity during inspection cycles helps reduce economic disruptions and safety issues.
The safety of military helicopters can be improved through advance warnings of potentially catastrophic failures, for example, in rotors.
The invention of the predictive maintenance technique is attributed by most to CH Waddington back in the second World War when he noticed that planned preventive maintenance appeared to be causing unplanned failures in the aircraft bombers.2 This led to the emergence and development of condition-based maintenance but since most business systems have historically been siloed, adoption of predictive maintenance has been limited.
Technological advances in IoT sensors, big data collection and storage technologies have and will continue apace. The growth of data and accessibility of AI/ML is enhancing predictive maintenance models and promoting its adoption. The pandemic also accelerated digital transformation efforts, creating more integrated business environments and appetite for intelligence-based real-time insights. Finally, the soaring cost of unplanned downtime, which experts estimate is around 11% of turnover in Fortune Global 500 companies3, is also fueling the adoption of predictive maintenance within the market.
The following technologies are just some of those contributing to the ongoing evolution and value of predictive maintenance:
Intelligent asset management, monitoring, predictive maintenance and reliability in a single platform.
Enhance your application performance monitoring to provide the context you need to resolve incidents faster.
Learn how Australian rail company Downer improved reliability by 41%
Read how IBM Research is furthering the development of predictive maintenance in technology and banking
Learn how IBM can support you in your 'journey to predict'.
Learn how predictive maintenance in the cloud is helping businesses improve performance
Read how Oncor reduced power outages and keeps customers satisfied with the predictive maintenance
Learn how Amsterdam Airport Schiphol applied corrective and predictive maintenance to achieve fewer delays
Get the most value from your enterprise assets with Maximo Application Suite. It’s a single, integrated cloud-based platform that uses AI, IoT and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs.
2 https://www.easterneye.biz/a-complete-history-of-predictive-maintainence-its-place-in-the-world-today/ (link resides outside ibm.com)
3 The True Cost of Downtime 2022, www.siemens.com/senseye-predictive-maintenance (link resides outside ibm.com)