Four Main Types of Preventive Maintenance
Time-Based Maintenance (TBM)
Usage-Based Maintenance (UBM)
Predictive Maintenance (PdM)
Prescriptive Maintenance (RxM)
Each serves its separate function in asset management, and together minimize operational risk. Let’s explore them in detail.
Time-Based Maintenance (TBM), also referred to as calendar-based or scheduled maintenance, consist of activities performed at regular, predetermined intervals without taking the equipment's condition into consideration. It is a common method used in sectors that require machines or assets to be performed regularly in order to avoid deterioration.
TBM, on the other hand, schedules maintenance tasks, such as inspections, lubrication, part replacements, and system checks, at predetermined time intervals — weekly, monthly, quarterly, or annually — based on manufacturer recommendations or understood best usage practices within industries.
In the automotive industry, for example, oil changes, tire rotations and fluid replacements are lined up to occur after a predetermined period or distance. Like HVAC systems in commercial buildings which have regular filter changes and inspections performed on the machines at pre-determined intervals.
Diagnosability: Maintenance schedules have been established, ensuring that equipment is serviced promptly.
Reduces Major Curbs: Regular maintenance decreases the chance for surprises breakdowns.
Ensures Compliance: High-regulation industries like healthcare and aviation need rigid maintenance schedules.
Preventative Maintenance: Letting things break before you look at them can save money and avoid unnecessary maintenance.
High Operational Costs: Regular planned maintenance could require personnel and materials leading to an increase in operational costs.
Doesn’t Include Real-Time Wearing: Machinery can break down ahead of the next planned maintenance, which can create some unanticipated downtime.
Usage-Based Maintenance Scheduling it based on the measurable usage of an asset rather than by time. This ensures servicing takes place when required, rather than on an arbitrary timetable.
UBM (usage-based model) — UBM bases the maintenance on the usage data, including operating hours, production cycles or mileage. Sensors and monitoring systems assist in monitoring these metrics and trigger maintenance activities only when established usage thresholds are exceeding.
For example, an industrial generator might need servicing every 500 operating hours compared to every three months. Likewise, aircraft parts are maintained based on hours in the air, if applicable, not dates on a calendar.
Appropriate Maintenance Scheduling: Minimizes unnecessary maintenance by scheduling tasks based on real equipment usage.
Labour cost: Reduces overhaul and replaces parts before their expiry date.
Enhances Life of Equipment: Maintenance is done only when needed which lessens wear and tear due to too much servicing
Monitoring Systems Needed: Implementation works only if you have a system that records usage data, and creating such systems can be expensive to implement and run.
Unanticipated Breakdowns: Although usage-based maintenance incorporates workload, usage-based maintenance may not identify sudden component failures.
Predictive Maintenance uses advanced technology tools, such as sensors, data analytics, and machine learning, to determine when equipment is likely to go down. This approach moves maintenance from a time-driven model to a data-driven model, enabling maintenance to be carried out exactly at the point when it is needed.
Predictive Maintenance analyzes real-time data from equipment sensors to identify anomalies, wear patterns, and performance trends. Machine learning algorithms sift through this data to predict potential breakdowns before they happen, allowing maintenance teams to step in before things go wrong.
For example, in industrial motors, vibration analysis can identify imbalances that signal bearing wear. Likewise, oil analysis in heavy equipment may show contaminants that indicate when lubrication has failed.
Minimizes Unscheduled Downtime: Catching problems before they become critical averts sudden equipment breakdowns.
Efficient Maintenance Schedules: Maintenance is done when it is required, rather than on a schedule, reducing waste and expenses.
Equipment performance is enhanced: Real-time monitoring enables proactive intervention, which enhances overall equipment performance.
Requires advanced monitoring tools, sensors, and data analytics software. With extensive experience in data analysis, the companies providing predictive coding systems come with a complex implementation, requiring skilled personnel to interpret predictive data and integrate systems effectively.
Predictive maintenance is dependent on accurate data; inaccurate data collection, in particular can lead to erroneous decisions on maintenance.
Formulated as advanced predictive maintenance, Prescriptive Maintenance not only predicts device failures, but also creates an action plan on how to recover from the predicted failures. It involves using artificial intelligence (AI) and the Internet of Things (IoT) to streamline maintenance methods.
RxM evaluates historical and real-time equipment data to identify problems and recommend fixes. RxM differs from Predictive Maintenance in this latter case because it offers detailed operational maintenance recommendations (for example: changing machine parameters, changing parts, or adjusting the operating conditions), rather than just pointing out that a potential failure may occur.
In a manufacturing facility, for example, an RxM system may identify an overheating motor and suggest reducing operational loads or enhancing cooling mechanisms. An AI-powered HVAC system, for example, may recommend adjusting airflow based on sensor data to relieve system stress.
Enhanced Decision-Making: Offers accurate recommendations on how to resolve maintenance problems.
Improve Equipment Reliability: You can keep the asset in top condition with minimum interruptions.
Cuts Down On Maintenance Costs – RxM provides targeted solutions that limit unnecessary repairs and part exchanges.
Requires the Integration of Advanced AI and IoT: Implementation requires a lot of high-tech infrastructure that can be costly.
Deep Data Analysis — Using AI-Driven diagnostics, RxM requires that the maintenance teams are trained.
Limited use by the industry: Although growing, RxM is still in the early phase of emergence in various industries.
It is also a key driver of longevity, reduced downtime, and increased operational efficiency. The four major types are Time-Based Maintenance (TBM), Usage-Based Maintenance (UBM), Predictive Maintenance (PdM), and Prescriptive Maintenance (RxM), which provide various different strategies for proactive maintenance.
The best preventive maintenance approach can vary depending on the organization’s needs, resources, and technological capabilities. With the right maintenance type in place, businesses can ensure equipment reliability, improve safety, and reduce operation costs.
