Along with the advancement of industrial technology, the method of maintaining motion control components of a machine, such as motors, drives, and sensors, has also evolved. The most common method of maintaining a machine after a failure has occurred was the traditional method. Realizing the need for improvement, maintenance personnel started to estimate life and replace motion control components before they fail. More recently, with the advancement of IIoT (Industrial Internet of Things) and real-time availability of status data, another method was born. While the two newer methods have the same goal in eliminating machine downtime, only one does it efficiently.
The chart above summarizes the process of the three methods. The red arrows show the process for the traditional "reactive" maintenance, and the blue arrows show the process for both "preventive" and "predictive" maintenance.
The difference between the three methods is how they determine exactly when to replace a component of a machine. In the traditional reactive maintenance method, a machine is maintained once a failure has occurred. However, as you can imagine, this "if it ain't broke, don't fix it" approach is not efficient and doesn't prevent downtime. For the rest of this post, we'll focus on "preventive" and "predictive" maintenance.
Both preventive and predictive maintenance methods can eliminate costly downtime. Both methods also use data to determine end of life for machine components. The differences start with the data. While preventive maintenance uses reference life data provided by manufacturers, predictive maintenance has the ability to use real-time data.
Let me introduce these maintenance methods by using some common analogies. Imagine the maintenance of your teeth. Preventive maintenance is when you follow a guideline that recommends a visit to a dentist's office every three months in order to "prevent" problems. Predictive maintenance is when you actually check your teeth in the mirror every day, then go to the dentist's office when symptoms occur. By observing for signs of tooth decay, you can "predict" and resolve a problem before it happens, and perhaps avoid a major surgery.
Now try to apply the same concepts to machine maintenance.
Preventive Maintenance uses guidelines from motion control manufacturers to determine when components should be replaced in order to prevent downtime. However, simply relying on manufacturers' recommendations isn't the best since manufacturers need to be conservative about referencing life for their products due to the many factors involved. This conservative life estimate can eliminate downtime, but it may incur more cost than necessary as it may replace components before their full life. For example, motor life is based on the service life of its bearings. However, bearing life is dependent on many factors, such as axial/radial loads, grease, and operating temperature. Oversizing a motor, or providing a bigger heatsink (or another cooling solution) for the motor will extend its expected life.
Since maintenance and resources can be planned at fixed intervals, the advantage of preventive maintenance is that it's easy to prepare for all departments involved.
Predictive Maintenance uses actual collected data and specific triggers to alert maintenance personnel of potential failures before they occur. Many motor and driver systems now offer built-in sensors or feedback to provide real-time status updates to a host controller (PLC/HMI/IPC) via an industrial communication network protocol, such as EtherNet/IP, EtherCAT, Modbus...etc. You can choose what data to collect. Once the data is uploaded, AI can take over and more accurately predict when a component will fail based on specific triggers (instead of conservative guidelines). This new advancement in communication and AI has paved the way for a more efficient method to eliminate downtime, replace motion control components at exactly the right time, and reduce costs of unnecessary replacements. These are the advantages of predictive maintenance.
One disadvantage of predictive maintenance is the necessary education for the maintenance personnel. A combination of IT and motion control skills would be make the job easier for the maintenance team. It's also important to partner with a vendor who can deliver components quickly.
Let's take a look at a common example of predictive maintenance. AI for healthcare has been trending lately. I've seen a whole market created for these smart wearable devices that can monitor the health of a person such as heart rate, steps taken, and calories burned. Some health monitoring devices can even perform electrocardiograms or provide estimates on blood pressure. Once you have a device that constantly monitors your health data, predictive maintenance takes over and alerts you when it's necessary to visit a doctor to avoid major diseases.
Without a health monitoring device, you need to rely on preventive maintenance and go to the doctors for a checkup every year or so. Similar to machine components, humans have different lifespans based on different factors, so triggers have to be customized as well.
In our personal lives, predictive maintenance is all around us to help us maintain our own lives. When the battery is low on your hand-held vacuum, it alerts you so you can recharge before using. When you're about to forget to pay a bill, automated alerts notify you before you are charged a late fee. With the introduction of the cloud, more data is available than ever.
Now back to the original question. Which maintenance method is more efficient?
|Which Method is More Efficient?
With predictive maintenance, it's important to customize the right triggers to indicate end of life. This is where experience will help. For example, you can estimate motor life simply based on mileage traveled, and you can program your PLC or AI to generate an alarm at a certain mileage for replacement. To provide a more accurate estimate, you can combine multiple triggers, such as if the operating temperature exceeded a certain level for x amount of hours AND if the mileage is over an x amount. It is this customization of triggers that really gives an advantage to predictive maintenance.
|Verdict: Predictive Maintenance|
With an estimated market share of 31.67%, North America will continue to be the biggest market for predictive maintenance solutions; with Europe following right behind. The below chart represents the predictive maintenance market share for 2017 and 2022.
Connecting the Data
Without the collection of data, there is no predictive maintenance. In order to collect data, some type of industrial communication protocol is necessary on a machine component, such as sensors or motion control axes. Once communication is established, then data can be logged and stored continuously. Once data from all axes of motion is collected, you will be able to know everything that's going on in a machine from a centralized location. The idea behind predictive maintenance is that you can make more informed data-driven decisions to replace components at exactly the right time.
Oriental Motor offers predictive maintenance solutions in the form of motors with built-in sensors as well as drivers with built-in industrial communication. For example, the AZ Series feature closed-loop feedback with a mechanical absolute sensor in the stepper motors to provide real-time status data and either Modbus RTU, EtherNet/IP or EtherCAT communication capability in the drivers. Protocols such as CC-Link, Mechatrolink, and EtherCAT can also be connected through a network converter. Real-time status data such as odometer, tripmeter, motor temperature, driver temperature, load factor, and alarm codes can be monitored. The AZ Series also offers a wide product breadth including motors, gear motors, rotary actuators and linear actuators to cover a variety of applications.
Watch as we demonstrate predictive monitoring capabilities with a push button endurance test machine.