Investing Predictive Maintenance

8 Reasons Why You Should Invest in Predictive Maintenance

Predictive maintenance is gaining traction as a method of reducing downtime and maintenance costs by using data analysis to identify potential issues before they occur. By predicting maintenance needs, businesses can avoid unplanned equipment failures and emergency repairs, improve equipment performance, and increase productivity and profitability.

There are many companies and investors currently investing in the predictive maintenance industry, with a focus on developing and improving the technology needed to implement this approach effectively. One of the key areas of investment is in the development of sensors and data collection systems, which are critical for gathering the data needed to predict maintenance needs accurately. Additionally, there is a growing investment in machine learning algorithms that can analyze this data and identify potential issues before they occur. Many companies are also investing in the development of predictive maintenance software platforms that can integrate with existing systems and provide real-time insights into equipment performance.

In terms of its current position, predictive maintenance is still a relatively new approach, and adoption rates vary across industries. However, it is becoming increasingly popular in industries where equipment reliability is critical, such as manufacturing, energy, and transportation. As the technology continues to improve and more businesses recognize the benefits of predictive maintenance, it is likely that adoption rates will continue to increase. The future looks promising for predictive maintenance as a key tool for improving equipment reliability and reducing maintenance costs.

Here are the reasons for you to invest in predictive maintenance:

1.Avoiding Costly Emergency Repairs

Predictive maintenance may save firms money by foreseeing maintenance requirements and resolving possible equipment problems before they become costly emergencies. Both in terms of repair costs and lost output from equipment downtime, emergency repairs can be costly. Businesses can use predictive maintenance to schedule repair during pre-arranged downtime, limiting the impact on output and lowering the possibility of unanticipated equipment breakdowns. Businesses may save money and keep their profitability by postponing urgent repairs. Predictive maintenance can also decrease the need for expensive emergency repairs and boost the general reliability of equipment, both of which contribute to long-term cost savings.

Emergency Repair

2.Extending the Lifespan of the Equipment

Predictive maintenance may assist organizations in addressing equipment problems before they worsen and cause irreversible harm by seeing possible problems early on. This can help equipment last longer and save the need for costly equipment replacements. Extending the lifespan of equipment can save replacement costs while also improving product quality since well-maintained equipment is better equipped to continuously produce goods of high quality.

3.Optimizing Maintenance Plans

Businesses may optimize their maintenance plans and cut down on needless maintenance expenditures by customizing maintenance schedules to the unique requirements of each piece of equipment. Businesses may utilize data and analytics to determine when maintenance is actually essential, lowering the frequency of maintenance and saving time and money, as opposed to doing routine maintenance on a set schedule. Businesses may minimize the chance of equipment downtime during maintenance and increase production uptime by managing maintenance plans in this way.

Maintenance Planning

4.Improving Equipment Reliability

Predictive maintenance can aid in boosting equipment reliability by promptly recognizing possible equipment faults and resolving them. This can lower the likelihood of unanticipated equipment failures and lessen the need for urgent repairs, increasing equipment uptime and boosting production.

5.Increasing Productivity and Profitability

Predictive maintenance may help organizations boost their production and profitability by reducing equipment downtime, prolonging equipment lifespan, optimizing maintenance schedules, and enhancing equipment dependability. This may result in an improvement in product quality, a reduction in product turnaround times, and a competitive edge in the market. Also, organizations may cut their operational costs and boost their bottom line by avoiding expensive emergency repairs and lowering maintenance expenditures.

6.Increasing Sustainability

Predictive maintenance may assist organizations in lowering their environmental impact and raising their sustainability by optimizing maintenance schedules and prolonging the lifespan of equipment. Businesses may preserve resources and cut down on waste by lowering the frequency of maintenance and eliminating pointless repairs. Businesses may also save money on energy and resources by prolonging the life of their equipment and avoiding the need for regular replacements. Businesses may guarantee that their equipment is operating at optimal efficiency, minimizing energy usage, and lowering their carbon footprint by adopting a proactive approach to equipment maintenance.


7.Workplace Safety

Predictive maintenance can assist firms in enhancing worker safety by proactively detecting possible equipment concerns. Businesses may lower the risk of accidents and injuries at work by addressing possible safety issues before they worsen. Moreover, organizations may lower the risk of equipment-related accidents and foster a safer working environment by optimizing maintenance schedules and making sure that equipment is in good operating order. Businesses may safeguard their workers, lower the danger of expensive legal challenges, and show their commitment to creating a safe and healthy workplace by emphasizing safety via the use of predictive maintenance.

Workspace Safety

8.Short Return of Investment Time

The early return on investment (ROI) is one of the major advantages of using predictive maintenance. Businesses may immediately achieve cost savings and boost their profitability by minimizing equipment downtime, avoiding expensive emergency repairs, and optimizing maintenance plans. Also, firms may lower their capital expenditures and enhance their cash flow by extending the equipment’s lifespan and lowering the need for frequent replacements. Depending on the particular requirements of each organization, the ROI period for predictive maintenance might change, but in many circumstances, firms can reach ROI in a matter of months rather than years. This makes a predictive maintenance investment appealing for companies aiming to boost their operations and bottom line in a short amount of time.


There are several ways that predictive maintenance helps your business to grow and several reasons for you to invest in it. It is important to note that these reasons are not isolated from one another, and they can often overlap and compound on each other. Because each reason triggers the others. In an increasingly competitive market, manufacturers are trying to find a way that can boost their outcomes. Take your place in advance with predictive maintenance to lead the industry!

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 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.


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.

Digital Transformation in 5 Minutes

Today, digital transformation is an inevitable reality and necessity for businesses. Keeping up with this transformation is a prerequisite for businesses to achieve sustainable development. The fastest way to meet this requirement is probably to provide digital transformation mediums as a cloud service.

But in some cases, on-premise solutions are needed instead of cloud services. Many parameters such as habits, security, financial strategies play a role in this decision.

At Sensemore, we offer an end-to-end cloud service in the field of machine health monitoring and predictive maintenance. The IoT sensors we produce send the measurement data to our cloud application over the internet and all analysis and alarm reporting processes are carried out in the cloud application.

Although our focus is on cloud application and environment tools development, we still need to be able to respond to on-premise requirements.

Challenges of on-premise applications

Most of the challenges of on-premise application are caused by environment differences.

  • Distribution and migration of the application to be installed
  • Installing applications and library dependencies
  • Ensuring operating system consistency
  • Updateability

Many more topics could probably be added to this list, but even as it stands, it sums up the issue. The user company has to meet these requirements. For this, you need to contact and agree with the IT staff. Naturally, it is necessary to have an IT infrastructure that can provide this support.

Luckily, there is actually a technology that we know produces answers to many of these problems.


Docker is a technology that allows you to move and run your applications in a platform-independent and dependent-independent manner. When you dockerize your application you will have a docker image.

Fig. 1 Fan System Failure Diagram

Docker images can run in any environment with a docker engine installed.

Independent of the operating system and additional application and library requirements.

It feels so good as a developer to not care where your app runs 🙂

Real world scenario of installation on-premise docker application

We got the power of docker behind us. We wrote and dockerized an application that includes parts that will run on-premise.

In the end, there were only two requirements we requested from our client.

  • Internet connection
  • A server with Docker engine installed

We made an appointment for installation and went to visit the company. We downloaded our Docker application to the server and ran the following command.

~/sensemore-iot-management$ docker-compose up

Yes!, just one command. And all the rest magically went on without errors, thanks to Docker. The application was installed on the host computer within minutes, it ran without problem and the commissioning was completed.

With Docker, installations are fast, applications are accessible, secure, and scalable. Exactly the features expected from a digital transformation medium.