What Is Predictive Maintenance?
Predictive maintenance uses historical and real-time data to find operational anomalies caused by potential equipment defects. PdM maintenance prevents frequent maintenance issues, sudden outages due to machine failure, and unnecessary preventive maintenance needs. It also helps lower your costs caused by expensive maintenance activities.
PdM maintenance (PdM) uses AI technology to identify when a machine or a piece of equipment needs repair before breaking down. The centralized system monitors all assets through the IoT devices attached to the machines. These sensors track the machine’s vibration, temperature, production, and others, to quickly pick up warning signs.
Goals of Predictive Maintenance
Predictive maintenance is a part of a company’s maintenance strategy. It is different from the traditional maintenance approach of fix-it-when-broken. Predictive maintenance generates alerts in advance, helping the manager to undertake preventive measures before the issue happens. The system monitors machine anomalies to generate timely alerts.
Secondly, PdM maintenance lowers the chances of unexpected breakdowns. PdM approach employs near real-time performance and process data analysis. It combines data from different sources with analytics to predict failures with confidence.
Importance of Predictive Maintenance
Having a maintenance strategy is paramount in industries in reducing costs, eliminating unplanned outages, and improving efficiency. According to a study conducted by Delloite, a non-optimized maintenance strategy can lower the production capacity of an industrial plant by 5-20%.
Predictive maintenance has applications in a wide variety of industries including, manufacturing energy and utilities, transportation and logistics, healthcare, and aviation.
Key Components of PdM Maintenance
The core of predictive maintenance includes four primary elements – data collection, analysis, predictive modeling, and maintenance action.
1. Sensor Technology:
Sensors attached to equipment monitor various parameters, such as temperature, vibration, and pressure, providing crucial insights into the condition and performance of assets. Information gathered from sensors is stored in a centralized dashboard of the asset management module.
2. Data Analytics and Machine Learning:
Data analytics and machine learning are critical for processing the large amount of data generated by the sensors. The maintenance management module of ERP software lets you leverage technology to identify patterns and track historical performance to generate accurate alerts.
3. Analysis and Predictive Modeling:
The combination of AI and ML sets predictive maintenance apart. Predictive maintenance is the analysis of historical data and current data to make intelligence decisions.
Predictive maintenance helps reduce unnecessary expenses towards maintenance activities.
Historic Data + Current Data = Intelligent Decisions
4. Maintenance Action:
It allows planning maintenance actions in a timely fashion, resulting in improving overall productivity, lowering spare part costs, improving economics, and overall resulting in a sustainable and resilient business framework.
Types of Predictive Maintenance Technologies
The key predictive maintenance types include:
1. Vibration Analysis:
Vibration analysis is common among the types of predictive maintenance.
Vibration analytic devices allow you to monitor machine performance such as rotating speed, misalignment, etc. In predictive maintenance, the vibration monitoring device collects and analyzes information against a baseline to generate timely alerts.
2. Infrared Thermography:
Thermal imaging is a technique used in predictive maintenance to determine heating issues in machines. It uses infrared cameras to detect anomalies mostly in electronic components. Managers visualize and analyze heating patterns using thermal imaging machines to plan maintenance actions before the issue escalates.
3. Oil Analysis:
Oil analysis is critical for effectively testing machine oil conditions. The detectors enable maintenance professionals to analyze changes in the composition of lubricating oil.
Oil analysis helps predict fluid properties, contamination, and wear debris in machine oil.
4. Ultrasound Leak Detection:
Ultrasound leak detector sensors identify leaks in gas pipes and venting tubes. Some ultrasound devices can even detect time-specific wave signals. These sensors detect changes in air pressure and enable the maintenance team to pinpoint the issue. By analyzing the data, the team can determine the severity of the issue and plan maintenance.
How Predictive Maintenance Works
PdM maintenance is a dynamic process. It facilitates data-driven decision-making through machine learning and AI. Predictive maintenance uses monitoring techniques to detect faults in machines. The predictive maintenance workflow consists of the following stages.
1. Infrastructure Deployment:
It requires deploying monitoring hardware consisting of sensors, an acquisition system, and a software platform for data visualization and analysis. Once the infrastructure is determined, it follows the steps:
- Data collection
- Data analysis
- Determining condition indicators, and baseline
- Training machine learning model
2. Data Collection and Monitoring:
PdM maintenance collects data through the sensors embedded in equipment. These sensors track parameters such as variations in temperature, vibration irregularities, and performance metrics, providing a comprehensive view of the equipment’s condition.
3. Pattern Recognition Using Data Analytics:
Maintenance engineers use data analytics tools to analyze patterns and trends to determine anomalies. They use historical performance data and performance benchmarking techniques to compare machine conditions and plan maintenance.
4. Developing Predictive Model:
Using machine learning, the team develops algorithms to define predictive models to compare collected data. This model is then used for predictive failure in performance and generating alerts. The asset management module of an ERP allows the team to schedule maintenance tasks in a timely and cost-effective fashion.
5. Using Digital Twin:
Using a digital twin allows the team to analyze data in various predictive maintenance scenarios. Creating a digital twin in PdM maintenance is an approved practice where the team creates digital twins of physical objects to run different simulations with real-time data. It helps fine-tune the predictive algorithm and allows the team to identify anomalies in a quick time.
When Is Predictive Maintenance Suitable?
Like any other strategy, the success of PdM maintenance is not universal. In some situations, PdM may yield limited benefits. Companies trying to optimize their maintenance expenses should be aware of the conditions where predictive maintenance is not the best policy.
Predictive maintenance is suitable for machines that are critical for operation. You can avoid implementing PdM maintenance for machines that are not critical. For types of equipment that have failure mode, predictive maintenance can effectively predict defects in advance.
Predictive Maintenance Advantages
The following are the benefits of predictive maintenance:
1. Increased Equipment Uptime and Reliability:
Predictive maintenance (PdM) includes discipline in maintenance works by pre-planning maintenance activities to build a sustainable and resilient production system. It is useful to minimize maintenance downtime that can affect the production and profitability of the company.
2. Cost Savings:
Predictive maintenance can help save costs by reducing expenses incurred in unnecessary maintenance and extending the life of equipment. It gives you the clarity to plan maintenance actions, reduce spare part costs, and better allocate resources.
Although establishing and implementing a predictive maintenance strategy requires significant upfront costs, it helps lower expenses incurred in preventive maintenance.
3. Prevent Unplanned Downtime:
While you can anticipate probable failures in advance you can plan maintenance acts to avoid unplanned downtime and emergency repair activities. Predictive maintenance enables businesses to maintain consistency in production.
4. Extended Equipment Lifespan:
The extended lifespan of equipment allows you to save costs in long-term maintenance activities. Proactive maintenance practices contribute to the longevity of equipment and reduce the need for frequent replacement.
5. Improved Workspace Safety:
By detecting machine failures in advance, predictive maintenance contributes to improving workspace safety. It identifies possible machine failures and generates accurate alerts, lowering the chances of workspace hazards and accidents.
6. Increased Revenues:
You can derive long-term benefits when you can plan repair tasks on time. Despite higher initial costs, the predictive maintenance method has helped organizations achieve long-term revenue growth by lowering repair expenses and spare part costs and improving efficiency.
Predictive Maintenance Disadvantages
1. High Initial Costs:
Installation of IIoT (Industrial Internet of Things) devices, data analytical tools, and integration with the ERP system involves significant costs. Small and medium-sized companies may particularly struggle with the high initial cost.
2. Data Complexity:
The sensors used in predictive maintenance networks collect machine data of a complex nature. Without correct data analytical tools and training, the team may struggle to process the information and draw insights.
3. Skill and Training Requirements:
Predictive maintenance is a field of computer science and engineering. It requires a team well-versed in data analytics and machine learning to undertake complex maintenance tasks. It means, that small and medium-sized enterprises may face higher training costs.
4. False Alarms:
Any system can fail, likewise, predictive maintenance can generate false alarms. It can result in unnecessary false alarms and increase maintenance costs.
5. Equipment Compatibility:
It may be difficult to integrate the old machine into a predictive maintenance model because of technological incompatibility. Moreover, there could be challenges in ERP integration and increase your dependency on the technical team.
Predictive Maintenance vs Preventive Maintenance
Predictive and preventive maintenance can both help organizations optimize production efficiency and minimize emergency maintenance requirements. Understanding their difference will help you in selecting the right approach.
| Aspect | Predictive Maintenance | Preventive Maintenance |
| Definition | Uses data and analytics to predict equipment failure and schedules maintenance just in time. | Involves performing routine maintenance activities at scheduled intervals, regardless of equipment condition. |
| Timing of Maintenance | Just-in-time, based on predictions of potential failures. | At predetermined intervals, typically based on a calendar or set schedule. |
| Data-Driven | Heavily relies on data analysis, including sensor data and real-time monitoring. | Follows a routine schedule and may not require real-time data analysis. |
| Equipment Monitoring | Continuous monitoring using sensors and other tools. | Periodic inspections or servicing according to a predefined schedule. |
| Benefits | – Reduces downtime by addressing issues before they escalate – Lowers maintenance expended in the long run – Improves equipment longevity |
– Simple to implement – May work for both new and old equipment |
| Examples | – Vibration analysis for rotating equipment – Thermal imaging – Oil analysis for lubricating systems |
– Changing machine filters and spare parts – Changing machine lubricating oil periodically |
Predictive Maintenance Examples
Let’s understand how predictive maintenance can save costs and improve efficiencies with two predictive maintenance examples.
1st Example: Aviation Company
An aviation company can improve efficiency by adopting predictive maintenance. Through continuous monitoring, they can predict any issue that could lead to failure. Predictive maintenance would study the vibration, temperature, and oil efficiency of the engine.
The airline can schedule maintenance tasks proactively and increase the lifespan of critical components.
2nd Example: Manufacturing Company
Process digitization and rapid industry automation powered by Industry 4.0 provide manufacturing companies with massive amounts of data on machine performance. The predictive maintenance software notifies the maintenance team ahead of machine breakdown. It reduces factory inefficiency and enables improved maintenance planning.
FAQs
1. What Is Predictive Maintenance Meaning?
Predictive maintenance uses data analytics to analyze machine performance data to raise alerts before failures occur. It uses vibration monitoring devices, thermal imaging cameras, ultrasound sensors, and oil analysis to anomalies in equipment.
2. How Does PdM Maintenance Differ From Traditional Maintenance?
Unlike preventive maintenance, PdM maintenance helps you determine the severity of the issue and schedule maintenance works in advance.
3. What Types of Equipment Benefit the Most From PdM Maintenance?
Predictive maintenance is beneficial for
- Machines critical for production
- High-value machines
- Types of equipment with complex components
4. How Does Predictive Maintenance Improve Safety in Industrial Settings?
By identifying potential failures in machines and enabling the teams to schedule maintenance tasks, predictive maintenance can create a safer environment for workers.
5. Can Predictive Maintenance Be Applied to Older Equipment, or Does It Work Better With Newer Technology?
While it is easier to implement with newer machines. However, in the case of older machines problems related to integration and monitoring may arise. You may check all options before deciding the course of action.
