Predictive maintenance is a proactive approach to maintenance, allowing equipment to be serviced before it fails. In this article, I will discuss how predictive maintenance works, why it is important, and the six benefits of implementing this approach in your manufacturing operations. Additionally, I will share some popular case studies that highlight the effectiveness of predictive maintenance and how it can be based on IoT technology.
How Does Predictive Maintenance Work?
Predictive maintenance is a data-driven approach that utilizes advanced technologies such as machine learning, artificial intelligence, and IoT to analyze data collected from sensors, equipment, and other sources. This analysis helps identify patterns and trends that can indicate equipment performance issues before they lead to failures. This approach uses historical data and machine learning algorithms to make predictions about when equipment is likely to fail, which can help maintenance teams schedule repairs and minimize downtime.
Predictive Maintenance requires nothing but informal mathematical calculations to know when a machine needs repair or replacement; This allows maintenance to be performed in a timely and efficient manner. In addition, with the help of Machine Learning, facility managers will have more time to focus on necessary tasks instead of making guesswork.
Traditionally, facility managers have performed predictive maintenance work with the help of SCADA – a computer system used to collect and analyze real-time data. But this method requires manual coding thresholds, warning rules and regulations. It does not take into account dynamic device behavior patterns or contextual data related to manufacturing in general.
Instead, if Predictive Maintenance is built on Machine Learning algorithms, they are equipped with data such as information technology, operational technology, and production process information about the speed of production flow and how the machines synchronize with each other.
Why Is Predictive Maintenance Important?
Predictive maintenance is becoming increasingly important in the manufacturing industry due to the numerous benefits it provides. Firstly, it helps to reduce maintenance costs by allowing maintenance teams to focus on the equipment that needs attention, rather than performing unnecessary maintenance on equipment that is operating as expected. This can result in cost savings of up to 50%.
Secondly, predictive maintenance helps to reduce unexpected failures by up to 55%. By detecting issues before they lead to equipment failure, maintenance teams can schedule repairs and prevent downtime. This can help to improve production efficiency and reduce lost revenue due to equipment failures.
Thirdly, predictive maintenance can reduce overhaul and repair time by up to 60%. By identifying issues early, maintenance teams can schedule repairs and reduce the amount of time required for maintenance activities. This can help to minimize downtime and improve production efficiency.
Fourthly, predictive maintenance can help to reduce spare parts inventory by up to 30%. By detecting issues early, maintenance teams can order spare parts in advance and reduce the need for emergency spare parts. This can help to improve inventory management and reduce costs associated with spare parts.
Fifthly, predictive maintenance can increase mean time between failures by up to 30%. By detecting issues early and scheduling repairs, equipment can operate for longer periods of time without requiring maintenance. This can help to improve equipment reliability and reduce the need for frequent repairs.
Finally, predictive maintenance can increase operating time by up to 30%. By detecting issues early and scheduling repairs, equipment downtime can be minimized, allowing for more production time. This can help to improve production efficiency and increase revenue.
Statistical Findings from Automation Research Reports
Numerous research studies have been conducted to evaluate the effectiveness of predictive maintenance in the manufacturing industry. One such study by Deloitte found that companies that implemented predictive maintenance saw a 25% reduction in maintenance costs, a 70% reduction in downtime, and a 35% reduction in overall maintenance costs.
Another study by the Aberdeen Group found that companies that implemented predictive maintenance saw a 50% reduction in maintenance costs, a 20% increase in equipment uptime, and a 15% increase in overall equipment effectiveness.
Additionally, a study by McKinsey & Company found that predictive maintenance can reduce maintenance costs by up to 30%, reduce equipment downtime by up to 50%, and increase overall equipment effectiveness by up to 20%.
Most Popular Case Studies Using Predictive Maintenance
Predictive Maintenance (PdM) has been adopted by various industries worldwide, and many companies have reported significant benefits from implementing PdM strategies. Here are some of the most popular case studies using PdM:
- General Electric (GE) GE has been at the forefront of implementing PdM technology in its manufacturing plants. The company has reported a 25% reduction in maintenance costs, a 20% increase in machine uptime, and a 10% reduction in unplanned downtime by using PdM. GE has also implemented an IoT-based PdM system, which collects data from sensors installed on machines, analyzes the data using machine learning algorithms, and predicts when maintenance is required.
- Siemens Siemens has implemented a PdM system for its gas turbines, which uses AI and machine learning algorithms to predict equipment failure. The system has helped Siemens reduce maintenance costs by 30%, increase equipment uptime by 20%, and reduce unplanned downtime by 15%. The PdM system has also enabled Siemens to move from time-based maintenance to condition-based maintenance, which has resulted in significant cost savings.
- Ford Motor Company Ford Motor Company has implemented a PdM system for its production line equipment, which has resulted in a 35% reduction in maintenance costs, a 70% reduction in unscheduled downtime, and a 100% increase in equipment uptime. The PdM system uses machine learning algorithms to predict when maintenance is required and has enabled Ford to move from reactive maintenance to proactive maintenance.
- Delta Airlines Delta Airlines has implemented a PdM system for its aircraft engines, which has helped the company reduce maintenance costs by 15%, increase engine reliability by 20%, and reduce unscheduled downtime by 30%. The PdM system uses IoT technology to collect data from sensors installed on the engines, which is then analyzed using machine learning algorithms to predict engine failure.
Predictive Maintenance Based on IoT Technology
The Internet of Things (IoT) has revolutionized the way companies approach maintenance. IoT-based PdM systems collect data from sensors installed on machines and equipment, which is then analyzed using machine learning algorithms to predict when maintenance is required. Here are some of the benefits of implementing an IoT-based PdM system:
- Real-time monitoring IoT-based PdM systems provide real-time monitoring of machines and equipment, which enables companies to detect potential issues before they escalate into major problems.
- Condition-based maintenance IoT-based PdM systems enable companies to move from time-based maintenance to condition-based maintenance, which results in significant cost savings.
- Predictive maintenance IoT-based PdM systems use machine learning algorithms to predict when maintenance is required, which enables companies to perform maintenance before a breakdown occurs.
- Reduced downtime IoT-based PdM systems can significantly reduce unplanned downtime, as potential issues can be detected and resolved before they cause a breakdown.
- Improved efficiency IoT-based PdM systems enable companies to optimize their maintenance schedules, resulting in improved efficiency and increased productivity.
IoT-based Predictive Maintenance competes with the traditional method of time-based scheduled maintenance. Some say that an IoT-based solution is a better choice, because mechanical failures are often linked to random reasons (80%) rather than its age (20%).
There is a classic program for maintenance services, SCADA, but it only allows local execution – whereas IoT allows storing many terabytes of data and running Machine Learning algorithms on a large number of machines. calculated at the same time.
The data about the parameters is obtained by the sensors to which the device or devices are connected and undergoes many conversions. This is necessary to achieve the ultimate goal – a Predictive Maintenance application that will alert users to potential equipment and equipment failures. Let’s take a closer look at what these conversions are:
Devices or devices with sensors
In this step, we will define the key values of the device we want to monitor (such as temperature and voltage for the battery) and set the sensor to capture them.
Field Gateway
The data collected by the sensors cannot go directly to the Gateway cloud, so one more physical device is added to this chain – the Gateway field filters and processes the data.
Gateway cloud
Cloud Gateway receives information from the Field Gateway and enables secure transmission and connection with the various protocols of the Field Gateways.
data lake
The next step is the Data Lake, which speaks for itself. The data collected by the sensors appears on the raw network and thus still contains irrelevant or incorrect entries. It is represented by sensor readings measured at a given time. When there is a need for insight into the data stored here, it goes to the Big Data Warehouse.
Data Warehouse
In this step, the data is cleaned and structured, so it contains the parameters taken by the sensors along with temporal and contextual information about the types, locations and dates on which the parameters were taken. perform. Now it is ready to be fitted in the Machine Learning model.
Machine Learning Models
In the Machine Learning step, we can reveal hidden data correlations, detect anomalous data patterns, and predict future failures.
Web/Mobile Application
Finally, we can receive notifications and follow up on potential maintenance needs with the User Application.
To conclude, we would like to say that Predictive Maintenance is becoming increasingly popular among industries worldwide, and many companies have reported significant benefits from implementing PdM strategies. IoT-based PdM systems provide real-time monitoring, condition-based maintenance, and predictive maintenance, which can significantly reduce maintenance costs, increase machine uptime, and improve overall efficiency.