It has been years since manufacturers have been employing a time-based approach for equipment maintenance. There was a time when the age of the machinery was taken into consideration to plan for maintenance routine. The simple way to calculate it by considering the fact that the older the equipment, the more frequent maintenance procedures need to be executed. However, according to a study conducted by the ARC group, across the globe, only 18% of types of equipment have failed due to its age. On the other hand, 82% fail on a random basis. This goes to prove that the time-based approach is not the right way to go ahead. The final take is a piece of equipment that gets maintained despite its actual requirement.
To get rid of ineffective maintenance routines and costs that accumulate with it, manufacturers can leverage the Industrial IoT solutions in combination with data science. In the forthcoming sections of this write-up, we will look at the various ways IoT-based predictive maintenance can prove to be effective in increasing the efficiency of the manufacturing process.
You might have a question as to why is it essential to utilize an IoT solution if there is already an excellent old SCADA to support the maintenance activities? The possible answer to this question is, predictive maintenance needs the capability to process lots of data and execute complicated algorithms.
All these become difficult to achieve if you are using local execution within SCADA. On the other hand, an IoT-based solution can store terabytes of data that can assist in executing machine learning algorithms across different computers. It is even possible to forecast potential problems and also note where the industrial equipment is deemed to fail.
When it comes to a robust IoT-based predictive maintenance solution, a well-contemplated architecture is mandatory.
Here are some of the components that assist in the smooth functioning of predictive maintenance. Let’s now divert our attention to IoT-based predictive maintenance architecture.
Before digging deep into the technical details, it is significant to ascertain essential variables that determine the health of the battery. This includes voltage, discharge, and temperature. Once that is over, the batteries can be used along with sensors to get the data of these parameters and get it to the cloud for further processing.
Sensor data does not have the capability of passing directly to the cloud. It needs to go through the gateways. There are two types of gateways. Field gateways can be defined as physical devices that clean and preprocess the data. On the other hand, cloud gateway is all about safe data transmission. It ensures connectivity through different protocols that enable connecting different field gateways.
When the sensor data enters the cloud part, it gets into the streaming data processor. Its primary goal is to enable the incessant flow of data and swiftly and effectively transmit data streams to a data storage – a data lake.
A data lake accumulates the data that has been assembled by sensors. It is in the raw state; hence, there is a possibility that it might consist of wrong, erroneous, or irrelevant items. Compute it with the help of different sets of sensor readings that can be measured at the corresponding time. Whenever information is required about the insights of the battery’s health, it can be found in a big data warehouse.
The big data collects all the filtered structured data. This includes voltage, discharge parameters, and temperature measured during a specific time and contextual details of batteries’ locations, recharge dates, types, etc.
After the data is prepared, it is scrutinized with the help of machine learning (ML) algorithms. The ML algorithms can be useful in gaining insights about hidden correlation in data sets and ascertaining abnormal data patterns. The approved data patterns are revealed in predictive models.
The purpose of predictive models is to ascertain whether self-discharge happens in a battery. It tells whether the batteries are functioning with a capacity lower than normal. Alternatively, it can even estimate the remaining useful life of the batteries. There are two ways to develop predictive models to predictively maintain industrial batteries.
Under this approach, models help to find out whether the battery is likely to self-discharge. You can even ascertain whether the capacity of the battery is lower than normal.
In this approach, the models provide information about the number of days or cycles that are left until the battery’s useful life ends.
Predictive models are updated regularly. For example, it can be every month and tested for precision. In case, there is variation in the desired output, they are revised, abstained, and tested again till the desired result is obtained.
Before getting upfront with machine learning, it is crucial to carry out adequate exploratory analytics. With the help of data analysis, it is possible to ascertain dependencies and find out patterns and insights into the machine learning data sets. On top of this, as a part of the exploratory analytics stage, there are certain technical assumptions that can assist in selecting the best-fit machine learning algorithm.
With the help of these user applications, it is possible to derive an IoT-based predictive maintenance solution that tells users about the potential battery failure. Also, predictive maintenance architecture can add extra components like control applications and actuators that are not connected to our battery example.
Based on the results achieved for the prediction, it is possible to set control applications to send commands to the actuators of the equipment. For example, if your engine’s temperature is rising to an alarming level, with the help of control applications you can send a command to cool-down the machine. You can even merge control applications with maintenance systems.
With the help of the universal architecture components mentioned earlier, it is possible to “build” predictive maintenance solutions for different industries. Let’s now focus on some of the predictive maintenance applications and look at how manufacturers have executed IoT-based predictive maintenance solutions in their respective industries.
There are quite a lot of discrete manufacturers that are employing predictive maintenance based on IoT for monitoring purposes. For example, consider the health of spindles in milling machines which are known to break easily. It is also quite expensive to repair them. With the help of an IoT-based maintenance solution, it is possible to foretell potential damage by accumulating data from ultrasonic and vibration sensors, which is connected to the spindle. Once you scrutinize the collected data, it assists in ascertaining fragile spindles before they break.
Process manufacturing firms like paper manufacturing companies utilize IIoT to take a note of the state of papermaking machines. You can also check out the example of the steel industry. Steel plants consist of a wide array of furnaces that employ water cooling panels to control the temperature. Due to leakages in the panel, there can be an occurrence of safety issues and production losses. Thanks to IoT-based predictive maintenance solutions, you can derive aberrations and find out the root cause of the problem that can go a long way in preventing production delays and equipment failures.
To sum up, IoT-based predictive maintenance prolongs the shelf life of the type of equipment. It is able to save as much as 70% of your time-based maintenance routine that overall assists in reducing equipment downtime by 50%.
However, if you wish to go for a mature and reliable predictive maintenance solution, it is vital to contemplate developing an architecture that is concentrated solely on machine learning. If you wish to learn more about IoT-based predictive maintenance, get in touch with a professional IoT service provider today!
About Author:Harshal Shah is CEO at WebITGurus, WebITGurus is a best IOT Services.Company offering .Net Applications Development Services like, SharePoint , C#, WCF Solutions and Asp.net Development.