Imagine living in a world where everything operates on production lines like clockwork and at peak efficiency. Imagine getting a helpful heads-up a few days before any critical equipment fails, rather than unplanned breakdowns and expensive downtime. Your maintenance crew would be informed, giving them plenty of time to prepare for the required maintenance or repairs.
This is the world that a condition-based maintenance program for manufacturing and production businesses today aspires to create in an ideal scenario. By using this strategy, businesses can gain a better understanding of the idea, the benefits it offers, the difficulties it might encounter, and the various applications and strategies available. This thorough manual aims to assist you in navigating these elements and making knowledgeable decisions when choosing assets for condition-based maintenance.
What Is Condition-Based Maintenance?
Condition-based maintenance (CBM) is a smart approach that keeps an eye on the real-time health of an asset to decide when maintenance is needed. Instead of sticking to rigid schedules, CBM suggests that maintenance should only be carried out when specific signs indicate a decline in performance or an imminent breakdown. These signs can be identified through various methods such as non-invasive measurements, visual inspections, performance data analysis, and scheduled tests. By collecting condition data periodically or continuously through internal sensors, CBM can be applied to both critical and non-critical assets.
To implement condition-based maintenance effectively, teams often rely on computerized maintenance management system (CMMS) software. This modern software, hosted on the cloud, can access asset data from sources like vibration sensors and PLC or SCADA systems. By connecting maintenance activities with reliability engineering and production monitoring data, CMMS integrations enable the system to generate alarms and automated work orders whenever faults or failures are likely.
Maintenance and reliability practices are evolving in the age of AI, the Industrial Internet of Things (IIoT), and smart factories. Strategies like condition-based maintenance, supported by technologies such as CMMS software, are at the forefront of this evolution.
What’s the Goal of Condition-based Maintenance?
Condition-based maintenance aims to monitor equipment conditions and predict upcoming failures, enabling proactive scheduling of maintenance tasks when necessary, rather than reacting after a breakdown. By triggering maintenance before the asset fails or performance declines significantly, CBM helps optimize asset performance and reduces unplanned downtime.
To implement successful condition-based maintenance, other elements of the maintenance operation need to be in place. This includes having a scheduled maintenance strategy for inspections and anomaly detection, as well as timely follow-up work orders. Predictive maintenance, powered by AI, can be the next step in identifying work orders that may lead to asset failure, using historical data and artificial intelligence for forecasting parts requirements.
How Does Condition-based Maintenance Work?
Equipment maintenance is a crucial aspect of ensuring the smooth functioning and longevity of machinery. To effectively manage maintenance tasks, equipment maintenance supervisors or facility managers establish a set of condition-based maintenance (CBM) indicators. These indicators serve as triggers for maintenance personnel to perform necessary tasks when specific conditions are met. Standard operating procedures (SOPs) are essential for providing step-by-step instructions and guidelines to assist maintenance personnel in addressing issues.
When Do We Use CBM?
Maintenance programs encompass various approaches, and organizations must strike the right balance to achieve optimal effectiveness. Condition-based maintenance is particularly valuable for applications that require continuous or regular monitoring of factors that can reliably indicate impending failures. Such applications typically involve critical assets necessary for optimal production line performance or employee safety. Some common scenarios where condition-based maintenance is applicable include:
- Temperature-Sensitive Assets: Temperature serves as a significant and easily measurable indicator of equipment performance. This application is versatile and finds use in numerous situations. For instance, in the food industry, maintaining safe temperature ranges in freezers ensures quality and safety. Similarly, keeping motors within acceptable temperature limits prevents overheating, while monitoring building temperatures optimizes employee comfort and energy conservation.
- Pressure-Sensitive Applications: Organizations relying on assets that utilize pressured air, water, or gas benefit from sensors capable of continuous pressure readings. These sensors not only detect minor leaks promptly by monitoring pressure drops but also prevent major damage and employee injuries by mitigating high-pressure situations as soon as they occur.
- Oil-Reliant Equipment: Condition-based maintenance through oil analysis is commonly employed for vehicles and fleet equipment. Similar to regular oil changes for personal cars, analyzing oil viscosity and particle content helps predict the oil’s lifespan and determine optimal replacement timing for maintaining peak performance.
What is the Difference Between Condition-based and Predictive Maintenance?
Predictive maintenance (PdM) is work that is scheduled in the future based on sensor measurements and mathematical analysis. Condition-based maintenance (CbM), on the other hand, is work that is conducted at the precise moment when monitored parameters reach unacceptable levels.
|Condition-based Maintenance (CBM)
Predictive Maintenance (PdM)
Relies on real-time or periodic measurements and observations of equipment’s health and performance parameters.
Utilizes historical and real-time data, including sensor data, maintenance records, and other relevant information.
Observing current conditions and applying predetermined thresholds for maintenance actions.
Applying advanced analytics and machine learning algorithms to historical and real-time data.
Actions are triggered by specific condition indicators or thresholds being reached.
Actions are triggered based on predictions or estimates of future equipment failure or performance degradation.
|Time and Cost Efficiency
||Optimizes maintenance efforts by performing maintenance activities only when a problem is clearly indicated, reducing unnecessary maintenance and associated costs.
Minimizes unplanned downtime by predicting equipment failures in advance, improving overall uptime and reducing maintenance costs.
What is the Difference Between Preventive Maintenance and CBM?
Preventive maintenance is conducted on a regular basis, whereas condition-based maintenance is performed when needed based on sensor data.
||Triggers for Condition-Based Maintenance include time intervals or meter readings.
||Preventive Maintenance is triggered by warning signs of impending equipment failure.
- Maintenance work is performed only as needed, leading to efficient resource utilization.
- Improved prioritization of maintenance time, focusing on critical assets first.
- Fewer unplanned downtime events, ensuring smooth operations.
- Maintenance is predicted in advance, reducing the chances of sudden breakdowns.
- Improved automation of maintenance tasks, streamlining the process.
- Reduces maximum unscheduled downtime, enhancing overall productivity.
||Condition-Based Maintenance is suitable for highly critical production assets with high repair and replacement costs.
||Preventive Maintenance is ideal for assets that need to be run continuously even during maintenance to avoid disruptions.
Types of Condition-Based Maintenance Techniques
Vibration monitoring involves measuring vibration levels and frequencies of machinery to assess their health. This technique aids in detecting various problems, including imbalance, bearing failure, resonance, mechanical looseness and bent shafts. By analyzing vibration signals, maintenance personnel can identify issues such as unbalanced fan wheels or damaged bearing tracks, which manifest as increased vibration at specific frequencies.
Infrared thermography utilizes thermal imagers to detect radiation emitted by objects, converting it into temperature data and displaying a real-time image of temperature distribution. By comparing these images against baseline references, it becomes evident when an asset is becoming overheated. Infrared thermography is beneficial for monitoring the electrical and mechanical conditions of motors, inspecting bearings, examining refractory insulation, and checking gas, liquid, and sludge levels. It encompasses spot infrared thermographers, infrared scanning systems, and infrared thermal imaging cameras, each suited for specific measurement scenarios.
Ultrasonic analysis relies on sound to identify potential asset failures by detecting high-frequency sounds and converting them into audio and digital data. Depending on the method used, ultrasonics can detect mechanical issues like faulty bearings, lubrication problems, pump cavitation, damaged gears and electrical faults in motors. Contact (structure-borne) methods are employed for mechanical issues, while non-contact (airborne) methods detect pressure and vacuum leaks in compressed gas systems or electrical faults such as arcing and corona.
Oil analysis involves routine assessment of oil health, contamination, and machine wear. By analyzing various aspects like fluid properties, additive levels, viscosity, presence of destructive contaminants, and particle analysis, maintenance personnel can verify if a lubricated machine is functioning correctly and identify potential sources of issues.
The electrical analysis focuses on examining the power quality of assets by utilizing motor current readings from clamp-on ammeters. This method enables maintenance personnel to detect abnormal electricity consumption and identify assets receiving an abnormal amount of electricity.
Maintaining proper pressure levels within the equipment is vital for fluid, gas, or air movement through pipelines or hydraulic hoses. Continuous pressure analysis in real-time allows for the detection of sudden drops or spikes, enabling maintenance personnel to promptly respond and rectify issues before they escalate.
Advantages And Disadvantages
|CBM is performed while the asset is working, reducing disruption to operations
||Condition monitoring test equipment can be expensive to install, and analyzing databases incurs costs
|Reduces the cost of asset failures
||Analyzing collected data and generating insights often requires specialized knowledge and expertise
|Improves equipment reliability
||Training staff to analyze data and perform CBM work can incur additional costs.
|Minimizes unscheduled downtime due to catastrophic failure
||CBM measurements may not easily detect fatigue or uniform wear failures
|Minimizes time spent on maintenance
||Condition sensors may not withstand harsh operating environments
|Minimizes overtime costs by scheduling maintenance activities
||Retrofitting assets with sensors may require modifications, incurring additional expenses
|Minimizes requirement for emergency spare parts
||Maintenance periods may still be unpredictable, leading to potential disruptions
Challenges of Condition-Based Maintenance
Significant Initial Cost
One of the primary challenges of CBM is the substantial upfront investment. Conducting a criticality analysis and determining sensor placement can be costly, especially when retrofitting older assets. To maximize return on investment (ROI), performing a thorough criticality analysis becomes crucial. For smaller plants lacking expertise, enlisting external professionals for failure mode and effects analysis (FMEA) and reliability-centered maintenance (RCM) adds to the expense. Moreover, selecting the right sensors tailored to operating conditions adds to the overall cost.
With real-time data from sensors, trained personnel are essential to analyze and interpret the information accurately and quickly. Each sensor alert generates several questions, such as the need for part replacement, part availability, time before asset failure, and vendor requirements for replacements. Training staff requires additional investment, and it may disrupt regular operational duties. Managing and integrating this change within the organization can be challenging.
The accuracy and effectiveness of sensors are influenced by the environmental conditions in which they operate. Extreme temperatures, humidity, or corrosive chemicals can lead to sensor malfunctions or inaccurate readings, hampering the success of CBM.
Unlike scheduled maintenance, CBM relies on real-time data, leading to unpredictable maintenance schedules. Handling maintenance tasks based on sensor alerts can create irregularities in budgeting, especially if multiple assets require attention simultaneously. Ensuring efficient management of repairs in such scenarios is critical.
Sensors continuously collect massive amounts of data, necessitating a modern computerized maintenance management system (CMMS) or suitable software for data organization, tracking, collection, and analysis. Utilizing third-party expertise until the in-house staff is fully trained can further add to the expenses. Adequate Wi-Fi connectivity and ample cloud storage are also essential considerations.
How to Optimize Condition-Based Maintenance?
- Find Your Baseline: You should start by identifying critical assets and establishing baseline performance metrics. Understand what factors need monitoring and set acceptable ranges for parameters like temperature, frequency, and pressure. Use historical data and manufacturer guidelines to calibrate sensors and analysis programs accurately.
- Create a P-F Curve: Employ the P-F curve, a visual tool to understand asset health over time. By analyzing asset deterioration, costs, and potential failures, you can pinpoint the optimal time for condition-based maintenance. Effectively timing maintenance interventions is the cornerstone of a successful CBM program.
- Implement AOM Technology: Asset operations management (AOM) technology integrates maintenance, operations, and reliability data, enabling informed business decisions. You can Utilize AOM to align with manufacturer guidelines and historical repair data, determining appropriate maintenance tasks and frequencies. The AOM software helps you to record sensor data, initiates work orders, facilitates repairs, and maintains an accurate asset history.
- Build the Right Culture: It’s important to foster a culture that values CBM and invest in ongoing training for staff, technicians, and employees. You must also empower technicians with knowledge and encourage proactive attitudes towards improvement. Ensure everyone understands the significance of condition-based monitoring for critical assets and the importance of timely repairs to prevent failures.
NEXGEN CMMS for Maintenance Planning
NEXGEN offers a user-friendly CMMS solution to simplify maintenance planning. You can schedule preventive maintenance tasks on a customizable basis and track your upcoming and completed work orders with comprehensive overviews and reporting. With real-time insights, NEXGEN enables efficient maintenance planning and management. NEXGEN puts condition-based maintenance at your fingertips. Try NEXGEN’s CMMS. Schedule a demo today!