Banner's motor vibration and temperature predictive maintenance system is an economical predictive maintenance solution suitable for a wide range of applications. Based on actual motor vibration and temperature data, Banner's solution assesses when maintenance is needed, preventing unexpected equipment failures.
First, why is motor vibration and temperature monitoring considered predictive maintenance? Let's look at the classification from the Intelligent Maintenance Systems (IMS) center in the United States, as shown in the diagram below. The vertical axis represents the frequency of failures, and the horizontal axis represents the impact of failures. Predictive maintenance is suitable for failures that occur infrequently but have a significant impact once they occur.
Motor failures during normal operation of equipment or production lines are low-frequency but high-impact failures that fall entirely within the scope of predictive maintenance.
In the industrial sector, motors are the power source for equipment and production lines. Monitoring the motor status of critical machines can prevent process downtime and maximize return on investment.
Prevent motor failures, reduce downtime due to malfunctions, and reduce maintenance and overhaul time and maintenance intervals;
Monitoring motor vibration and temperature can help predict motor failures, reduce downtime, and allow engineers to schedule and initiate repairs when the machine is offline or in hibernation, extending maintenance intervals and improving the service life of the motor.
Reduce maintenance and spare parts costs;
Predictive analytics can often identify motors that require attention, allowing factory technicians to adjust tool and spare parts inventory as needed, thus saving time, money, and space on the factory floor.
Improve workplace safety
Predictive maintenance can reduce or avoid the serious risks to worker health and safety caused by equipment failures due to motor malfunctions, thereby reducing significant losses in time, productivity, and profits.
Improve quality and efficiency, reduce costs and inventory.
Predictive maintenance of motors reduces sudden equipment failures that could damage goods ready for shipment or distribution; it also reduces downtime of CNC machine tools or process control equipment during production, preventing waste of specific parts and their constituent raw materials. This improves the overall efficiency (OEE) of equipment or production lines, thereby enhancing product quality, increasing production efficiency, reducing production costs, and minimizing inventory of raw materials and semi-finished products.
With the increasing adoption of predictive maintenance, in the past two years, more and more end users have raised the need for motor vibration and temperature monitoring, and more and more manufacturers and integrators have also proposed their own predictive maintenance solutions for motors. However, many customers have certain concerns about the implementation and effectiveness of motor predictive maintenance solutions, and Banner's motor vibration and temperature maintenance system can precisely eliminate end users' concerns about various risks. Common concerns of end users include:
1. Lack of technical experts; model accuracy needs improvement.
A mechanistic model is a precise mathematical model built upon the internal mechanisms of an object, a production process, or the transmission mechanisms of material flow. This model is one of the foundations for predictive maintenance, but data-driven thinking in industry is still in its early stages, and experts who understand the mechanisms are a minority.
Predictive maintenance requires a solid understanding of the underlying mechanisms and industry know-how. Determining which data to collect, how to install sensors, and how to select the collection frequency and cycle all depend on mastering the underlying mechanisms and industry know-how.
Outputs the root mean square velocity (RMS) value of the vibration. Users can perform motor health status analysis based on the RMS value without requiring technical experts or mechanistic model studies.
Banner vibration temperature sensors come with a magnetic base for easy and quick installation!
2. Poor data portability
For industrial enterprises to truly achieve digitalization, they must have a comprehensive understanding of the data they possess, maintain complete and continuously updated data visibility, and migrate data as needed based on business requirements.
Meanwhile, the ownership and usage rights of industrial data have always been a sensitive topic. Categorically, industrial data can be broadly divided into two types: equipment data and operating condition data. Operating condition data involves internal business information, and end users do not wish to share this data.
Motor vibration and temperature data fall under the first category of equipment data, which is generally considered non-sensitive data. Of course, Banner also fully respects user wishes and can provide both localized and cloud-based solutions based on user data requirements.
Localization solutions
Banner's Data Watch software allows for customization of the interface to meet specific customer needs. It provides features such as real-time data display and historical curve review.
cloud solutions
Cloud platform: Provides services such as data viewing, trend analysis, historical data storage, and email alerts.
3. Suppliers pose significant risks.
After predictive maintenance was touted as a "killer app," many companies have entered the field. However, some have been misleading, offering predictions that a device will malfunction, which end users may question.
Banner's vibration temperature sensor directly outputs the root mean square velocity value of the vibration, which conforms to the international standard ISO10816, completely eliminating users' concerns! The following figure is a comparison of normal and abnormal data of the main spindle motor of the stamping workshop of a large domestic joint venture car factory. Monitoring point 1 is normal, while monitoring point 2 is abnormal, with a significant increase in vibration data, which can be intuitively judged as an abnormality of the main motor!
4. Potential transaction risks and switching risks
Whether it's end users or equipment manufacturers, when looking for technical partners for predictive maintenance, they will face agreement conversions and trade-offs in technical means. In this process, they may become tied to the technical service provider.
Banner's vibration and temperature sensors offer the standard Modbus protocol and support touchscreens and configuration software like Datawatch for communication with over 300 mainstream PLCs and controllers. This allows for quick and easy integration of motor monitoring data into users' control and information systems. Data can also be pushed to users' cloud platforms via standard protocols such as HTTP, HTTPS, and MQTT.
In summary, Banner's motor vibration and temperature predictive maintenance system is accurate, simple, intuitive, and flexible, perfectly addressing end-users' concerns and anxieties regarding the implementation and effectiveness of predictive maintenance!
Most existing predictive maintenance solutions on the market operate at the cloud or fog computing level. However, with the improvement of edge computing power and the development of industrial artificial intelligence, performing predictive maintenance at the edge has become more economically feasible. Banner's DXM series wireless controllers can be equipped with industrial artificial intelligence algorithms and can also realize cloud data push, meeting users' needs for predictive maintenance of motors and used to diagnose and predict motor anomalies.
Of course, even with a predictive maintenance system, regular motor maintenance is still important. To prevent motor burnout, the following points should be noted during routine maintenance:
1. Keep the motor clean regularly, especially the contact points and coils.
2. Keep the motor secure, especially the foundation bolts and bearing end cap bolts.
3. Operating under rated load
4. The three-phase currents must be kept in balance.
References:
Is predictive maintenance the shortest path for edge computing and artificial intelligence to be implemented in industry? [IoT Insights] Peng Zhao IoT Think Tank
2019 Hannover Industrial Intelligence New Trendsetter King Tianyu Xuanbing Knowledge Automation
Predictive maintenance brings 5 major benefits to manufacturing! How does predictive maintenance reduce factory costs? (Excerpt from Industrial Intelligence)
Practical Tips: Six Preventive Measures for Electric Motor Burnout (Transmission Network)
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