Importance of Integration of Condition Monitoring into Existing Systems
We talked about condition monitoring and its importance in our previous blog post. The basic principle in condition monitoring is to detect failures in machinery and equipment to predict using equipment-specific condition parameters, such as temperature, vibration, pressure, current, etc. and to prevent destructive downtimes. One of the most important factors affecting the performance of condition monitoring applications is the ability to integrate into existing systems.
When we talk about an industrial telemetry system, the first terms that come to mind are; there will be SCADA (Supervisory Control and Data Acquisition), DCS (Distributed Control System), PLC (Programmable Logical Controller), and RTU (Remote Terminal Unit). At this point, the fact that condition monitoring systems talk to such traditional data collection and control systems increases the success rate of the condition monitoring system significantly. The communication here should be in the form of both data sharing from IIoT platform condition monitoring systems to traditional PLC-SCADA systems and data sharing from traditional PLC-SCADA systems to condition monitoring systems.
Fig. 1 PLC-SCADA Systems to Condition Monitoring Systems
The first of these integrations’ value propositions in terms of predictive maintenance; is to optimize measurement strategies. In other words, prevention of data pollution. Trying to collect data continuously, especially in equipment that works intermittently, working at variable speeds and loads, creates meaningless data pollution. The data collected in systems that cannot be correlated with each other or that require very different parameters to be correlated becomes meaningless and dirty. There are many different methods to overcome such problems, but first of all, it is necessary to understand the nature of the system correctly and to get the maximum benefit from the integration with the right methods.
Let’s explain these cases one by one by going through them;
Variable Speed Machines
In order to create the desired process parameters in many sectors from FMCG to Automotive, from Energy to Iron-Steel and Metal Industry, rotary equipment is constantly working at different cycles according to these process parameters. In this case, the most basic method used in predictive maintenance creates problems in vibration analysis. False-positive alarms may occur as vibration values will change according to the equipment cycle. On the other hand, it may give wrong outputs in the calculations of the root cause of the failure and the remaining useful life. In this context, it is insufficient to set upper/lower limits or observe deviations, which are the most basic methods to be followed.
Fig. 2 Variable Speed Machine Vibration Signal and Spectrum
On the other hand, it may give wrong outputs in the calculations of the root cause of the failure and the remaining useful life. These are parameters that can change very easily with dozens of factors, from the fact that the environment in which the equipment is located is different from the environment in which it is tested, to the way the operator uses the machine. At this point, the integration of online condition monitoring systems with traditional data collection and control systems constitutes the key solution.
Performing the analysis of the data by taking measurements under constant environmental and process operating conditions will allow you to reach optimum results. For instance; the characteristics of vibration signals received from an electric motor operating at 1500 RPM and the characteristics of vibration signals of the same electric motor operating at 2500 RPM are very different. In determining the root cause of the malfunction, the vibration signal alone will be insufficient. The technique used in vibration analysis is harmonic analysis, that is, the root causes of failure are estimated by examining the equipment excitation frequency and harmonics. At this point, since the harmonics at 1500 RPM and 2500 RPM will be at different frequencies, it will not be possible to predict the root cause of the fault. With the integration of online condition monitoring systems with traditional data collection and control systems, it is possible to take measurements in certain cycles by giving a speed, etc. parameters can also create the trigger mechanism on the condition monitoring system. Similarly, predictive maintenance outputs can print both measurement metrics and machine learning outputs to data acquisition systems such as SCADA.
Non-Continuous Machines
Although condition monitoring systems are generally applied to machines that operate continuously with high downtime costs, when criticality analysis is made, condition monitoring systems can be applied to machines working in shifts or machines that make stop-starts if they are more critical. In this type of machine, the measurements taken at certain periods are insufficient. While it causes the measurement to be missed, on the one hand, it also negatively affects battery power consumption in wireless sensors, as mentioned above. With the integration of PLC-SCADA systems into the condition monitoring systems, optimum results can be obtained with the SCADA system triggering the status monitoring system while the machine is running.
Fig. 3 Non-Continuous Machine Vibration Trend
Integration of condition monitoring systems into PLC-SCADA systems is not only at the point of triggering and optimizing measurement periods, but also at the point of sharing all collected data with condition monitoring systems, along with current process parameters and predictive analytics applications, general equipment efficiency, and production and maintenance outputs such as key performance indicators. It is also possible to monitor and control online.
Machines with Impulse and Transient Responses
Packaging machines, presses, cam mechanism machines are widely used in all kinds of industries from automotive to pharmaceutical industry, from fast moving consumer goods to white goods. Although this type of machines can work continuously and at constant speeds, they are equipment that make predictive maintenance applications difficult due to both the fact that they have a complex mechanism and the mechanical effect they create. For example, the data received from the accelerometer placed in the electric motor of a pressing machine may generate high vibration values due to the mechanical effect of the press impact, even if there is no fault in the electric motor.
On the other hand, special packaging machines, which are used in different ways but for the same purposes in every industry, can create meaningless and distorted signals by creating temporary reactions during packaging. However, the basic parameters that can be used to detect the malfunctions that may occur in the rotating equipment of these types of machines at an early stage are also vibration, temperature, force, etc. mechanical metrics. At this stage, in order to benefit from the predictive analytical power of condition monitoring systems, such parameters that IIoT sensors cannot provide but exist in PLC-SCADA systems should be integrated with condition monitoring systems. In this way, unplanned equipment stops are prevented by increasing the predictive analytical performance, and a more efficient decision-support mechanism is created.
Integration Options
In a business that already has a data collection system, you need to integrate IIoT sensors with new capabilities into your existing system. On the other hand, it is also possible to integrate existing metrics and records into the condition monitoring system. To exemplify, in order to benefit from the high analytical capabilities of online condition monitoring systems, it is necessary to share the existing system parameters with the status monitoring system, while the alarms created as a result of the analysis and estimations should be seen in the PLC-SCADA system and trigger the machine when necessary. There are many methods for integrating online condition monitoring systems into existing systems. These methods are explained in detail in our previous blog post.
Conclusion
With the integration of traditional methods into online condition monitoring systems, both periodic measurement strategies are optimized and fault root cause estimations are made more accurate. At this stage, both a more sustainable machine and higher profitability are obtained by making detailed predictive analytics applications, especially with the integration of process parameters into online condition monitoring systems.
It will become inevitable to establish communication between all interconnected processes. Until that day comes, it would be a right step to prefer integrated solutions and ensure that the investment made is long-term. If you do not have a digital transformation team or your technical skill set does not cover these developments, working with a provider who can support you at this stage plays a critical role in bringing the project to life.
IoT Integration in Digital Transformation
The popularity of the “Internet of Things” is increasing day by day since it is one of the most effective instruments of digital transformation in the industry. Undoubtedly, it is obvious that we need to use these solutions, but adding these instruments directly to our system is not as easy as it seems. Companies already have many data collection, analysis, and process tracking tools. It is quite challenging and impossible at some scales to abandon these systems in one day and switch to platforms and applications offered by new technologies. During the digital transformation period, hybrid models and old and new systems need to work together to make the transition smooth and risk-free. Therefore, it is expected that IoT/SaaS solutions will be able to act together with these systems. At this point, the most important topic is “merging”, in other words, “ability of integration”.
In this article, we will go into some technical details and talk about how we can integrate SaaS and IoT products into existing systems.
Sure, there is no one-size-fits-all solution for all the scenarios. The technical skills of your business, the methods offered by the IoT/SaaS solutions you provide, and the integration options of the system you currently use are the determining factors of the transformation strategy.
How the integration will take place mostly depends on how the data communication is going to take place. In other words, the question is where does the data flow from and to where? In addition, where the data is processed is another determining factor.
While expressing the integrations, we will continue by saying “SCADA” to the data collection / visualization system you already have, and “SAP” to the process/resource management systems.
One-Way Integrations
Master/Slave
Master/slave is a communication model for hardware devices in which one device has one-way control over one or more devices. This is usually used in the field of electronic hardware where one device acts as a controller while other devices are those that are controlled.
Let’s say you bought a temperature IoT sensor. The system you are currently using also allows device identification with Modbus/TCP protocol.
In this scenario, the integrated system is responsible for the management of the device. In other words, how often the measurement will be taken, where the measurement will be displayed/stored, all these tasks are the tasks of the system in which you integrate the sensor. At this point, the Master-Slave structure is one of the most frequently used integration methods. Of course, in order to make this integration, the integration method offered by the sensor and the methods of the SCADA system you use must match. It may be preferable if your concern is only data collection, monitoring, and basic alarm structures. On the other hand, IoT sensors that are getting smarter by the day require much more complex managerial skills. For example, it is unlikely that you will integrate wireless sensors into your system with this method.
Platform -> SAP (WebHook)
Let’s explain this scenario with an example:
You bought an IoT pressure sensor and there is a SaaS platform that comes with this device. You perform device management, configuration and measurement strategies through this software. Your expectation from the platform is to enable you to monitor the pressure values and to create a work order in your internal system by giving an alarm when it reaches a certain threshold value.
Several integration strategies can be implemented at this stage;
You can create an integration point in the SAP system to which alarm data can be forwarded. When the alarm occurs, the platform can send this request to the Web-Service (middleware) you have located and save it in the SAP database.
If you can perform the data analysis in your own system, you can also write a middleware software to integrate the sensor directly into your system, process the data there and interact with your SAP system accordingly. Presumably your service will interact with the sensors via MQTT/HTTP or any other protocol popular in the IoT world.
Scada -> Platform
In some cases, you want your system to interact with the platform.
For instance, you have positioned a vibration sensor on a press on the production line. You want the press to take a measurement on the time it hits. Since your existing system produces the “Press” command, you want to send this information to the sensor and enable it to take measurements.
If you have a Master-Slave application, it will be very easy to transmit this order, but it is important to remember that it will still be your responsibility to store, process and analyze the collected data. It should not be forgotten that the main reason for integration is to make your existing system smarter by supplying the workforce through external solutions.
On the other hand, you can forward this order using one of the integration points of the platform you are using. Most of the time this event is triggered by an HTTP Rest API endpoint. The platform can also initiate the measurement process by transmitting this request to the sensor. You can do this with a small HTTP Request that you add to your PLC system.
Asynchronous Integration
Thanks to message-based communication, it is possible to communicate two-way and asynchronously. You can think of it as a chat application. Both the output of the data from the SCADA system to the platform and the transmission of data from the platform to the sensor or to the SCADA system can be performed asynchronously. For this, it is useful to take a look at message-based communication.
In message-based applications, there is a message queue in the middle of the communication, this queue is managed by an external actor (broker). Parties wishing to communicate communicate with each other through this channel. In this way, they can communicate without knowing each other’s access points. Since the data will be stored in the queue, data integrity is also preserved.
Thanks to the message queues, making a two-way integration becomes very easy. The team integrated with the platform does not need to write a Web-Service or open a port to the internet.
As an example, you have positioned your IoT sensors on various electric motors. You want the sensors to take measurements only when you want them to. You will immediately provide the alarms and reports generated by the platform and display them in your own system.
You start the measurement processes on your own system. In addition, you are sending various data to the platform you are integrated with. For example, instantaneous rotation speed, oil information, machine model, last maintenance date. You send the measurement order and additional parameters to the queue via the integration leg, the platform receives the message in the queue and starts the measurement process, collects the data after the measurement via the sensor. It performs analysis and alarm checks using the additional parameters and measurement data you send. It puts the reports and notifications it produces in the queue. The integration service receives the message in the queue and performs the relevant actions in SCADA and SAP systems.
Conclusion
It is very critical that the instruments you will use in Industrial Digital Transformation are integratable. You do not want to choose a closed box system and undermine your digital transformation process by keeping it dependent on a brand or a system. Having a digital transformation team and IT staff will speed up these processes. If you do not have such departments, it is very important that you work with service providers who work with you in this process.
At Sensemore, we attach great importance to this issue. We try to keep almost all of our products integrated with external systems, and to support our customers’ processes by diversifying their integration options. Moreover, we do this with completely open source code. If you are wondering about the integration options Sensemore offers, you can take a look at Sensemore Documentation.
And you can contact us at any time.