What is Predictive Maintenance?
Predictive maintenance builds upon condition-based monitoring to enhance the performance and longevity of equipment by continuously assessing its health in real-time and predicting the possibility of failure in near future. Predictive maintenance methods usually provide estimated time remaining till the system fails.
By collecting data from sensors and utilizing advanced analytical tools and processes like machine learning (ML), predictive maintenance can identify, detect, and address issues as they arise. It can also forecast the future state of equipment, thereby reducing risk. The key here is delivering the right information at the right time (before failure happens) to the right people (maintenance staff).
Predictive Maintenance versus Preventive Maintenance
Maintenance strategies and their maturity depend on factors such as asset and replacement costs, the criticality of the asset, usage patterns, and the 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 are reactive maintenance, which addresses failures when they occur, and preventive maintenance, which relies on a predefined maintenance schedule to identify faults.
Being proactive, predictive maintenance enhances preventive maintenance by providing continuous insights into the actual condition of the equipment, rather than relying on the expected condition based on historical baselines. Corrective maintenance is only performed when necessary, avoiding unnecessary maintenance costs and machine downtime.
Predictive maintenance uses historical time-series and failure data to predict the future potential health of equipment, allowing 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 equipment. Techniques such as sound (ultrasonic acoustics), temperature (thermal), lubrication (oil, fluids), and vibration analysis can identify anomalies and provide advance warnings of potential problems. For example, a rising temperature in a component might indicate airflow blockages or wear and tear, unusual vibrations might indicate misalignment of moving parts, and changes in sound can provide early warnings of defects undetectable by the human ear.
How Does Predictive Maintenance Work?
Predictive maintenance relies on various technologies, including the Internet of Things (IoT), predictive analytics, and artificial intelligence (AI). Connected sensors gather data from assets like machinery and equipment, which is then collected at the edge or in the cloud within an AI-enabled enterprise asset management (EAM) or computerized maintenance management system (CMMS). AI and machine learning analyze 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.
Advancements in machine learning algorithms allow predictive maintenance solutions to predict the future condition of equipment. These predictions 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, giving businesses confidence that equipment is working optimally.
Benefits of Predictive Maintenance
The benefits of a predictive maintenance strategy include anticipating equipment faults and failures, reducing maintenance and operating costs by optimizing time and resources, and improving equipment performance and reliability. Deloitte reported in 2022 that predictive maintenance can result in a 5-15% reduction in facility downtime and a 5-20% increase in labor productivity. 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 results in fewer breakdowns and reduced planned or unplanned downtime. Greater continuous condition visibility enhances the lifetime reliability and durability of equipment. The use of AI can more accurately forecast future operations. This benefit is crucial in a world where rising prices and unpredictable events, such as the pandemic and climate-related natural disasters, have exposed the need for more predictable spare parts inventory, labor costs, and a lower environmental impact from operations.
Productivity can be increased by reducing inefficient maintenance operations. Faster responses to problems via intelligent workflows and automation, and equipping technicians, data scientists, and employees across the value chain with better data for decision-making, lead to improved metrics such as mean time between failures (MTBF) and mean time to repair (MTTR), safer working conditions, and revenue and profitability gains.
Predictive Maintenance Challenges
There are barriers to predictive maintenance, which can be costly, at least initially.
System Infrastructure
Startup costs associated with the complexity of the strategy are high. These often involve upgrading and integrating outdated technology and monitoring systems, as well as investing in maintenance and data management tools and the data and systems infrastructure.
Workforce Training
Training the workforce to use the new tools and processes and correctly interpret data can be expensive and time-consuming.
Data Requirements
The past is a predictor of future performance. For predictive maintenance to be effective, substantial volumes of time-series historical and failure (or proxy) data are vital. The ability to analyze data correlations and analogies with similar equipment types in physical operating conditions is essential and can also help improve predictive analytics.
Criticality and Cost Assessment
Assessing the criticality and cost of failure of individual assets takes time and money. However, this assessment is fundamental in determining whether predictive maintenance is appropriate — low-cost assets with cheap, readily available parts may be better served with other maintenance strategies.
Predictive maintenance programs are challenging, but the competitive and financial advantages of a well-run strategy are significant.
Industry Use Cases
Predictive maintenance technologies are already being adopted across industries for many assets, from cash points to wind turbines, heat exchangers, or manufacturing robots. Asset-intensive industries such as Energy, Manufacturing, Telecommunications, and Transportation, where unforeseen equipment failures might have widespread consequences, are increasingly turning to advanced technologies to improve equipment reliability and labor force productivity. Potential uses are many and varied:
Energy
Power outages can cost energy companies millions of dollars in compensation and lead to customers switching providers.
Manufacturing
Equipment failures and unplanned downtime can significantly increase unit costs and create supply chain disruptions.
Telecommunications
Fixing telecom network errors quickly is critical to improving service quality, even small network outages can impact many customers.
Railways
Identifying point or brake failures or track deformations prevents service interruptions and ensures passenger safety.
Civil Infrastructure
Better assessing structural integrity during inspection cycles helps reduce economic disruptions and safety issues.
Defense
The safety of military helicopters can be improved through advance warnings of potentially catastrophic failures, such as in rotors.
Future of Predictive Maintenance
The invention of the predictive maintenance technique is attributed by most to C.H. Waddington during the Second World War. He noticed that planned preventive maintenance seemed to cause unplanned failures in aircraft bombers. This led to the emergence and development of condition-based maintenance. However, since most business systems have historically been siloed, the adoption of predictive maintenance has been limited.
Technological advances in IoT sensors and big data collection and storage technologies are continuing 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, estimated at around 11% of turnover in Fortune Global 500 companies, is also fueling the adoption of predictive maintenance within the market.
The following technologies are just some contributing to the ongoing evolution and value of predictive maintenance:
Automated Robotic Inspection
Making monitoring of equipment in remote or dangerous locations more efficient and cost-effective. Robots act as roving sensors that monitor multiple assets and feed data into computerized maintenance management systems.
Immersive Technologies
Augmented reality (AR) and virtual reality (VR) are being developed to simplify inspections. AR can collect data, and both technologies can enhance visual inspections and early fault detection.
Digital Twins
Creating a virtual representation of a physical asset, generating sensor data, and simulating operational fault scenarios and solutions throughout an asset’s lifecycle with no risk to the asset.
IoT-Enabled Predictive Maintenance Solutions
Provided as part of EAM/CMMS solutions and integrated with other enterprise applications.
Predictive Maintenance-as-a-Service
Making predictive maintenance more accessible and affordable. Delivered by partners, it can be less disruptive than on-premise deployments, require less investment and training, and deliver faster time to value. It can also be tailored to individual environments and equipment.
