Technological advances have led companies from different sectors to incorporate predictive monitoring solutions into their machines and equipment. The promise of reducing downtime and optimizing maintenance is compelling. However, the in-house development of monitoring software with predictive analysis presents numerous technical challenges. Let's explore these challenges based on the experience of industrial equipment and machinery manufacturers.
Integration with Equipment and Devices
Establishing secure remote communication with machines and equipment is one of the biggest technical challenges faced by a manufacturer. This integration requires that software and hardware communicate effectively and stably, regardless of the type of technology, often using completely different protocols, depending on the model. Furthermore, there is a growing demand from customers for unified monitoring, which is independent of brand or model. The fragmentation of systems, where each type of equipment is managed by a different tool, proved to be unfeasible. Managing multiple systems not only increases operational complexity, but also makes it difficult to obtain a comprehensive, integrated view of asset performance.
Multisector Communication
Customers have become aware that it is impractical to manage multiple systems from different manufacturers. Increasingly, they demand unified multi-sector monitoring solutions, capable of managing equipment from different application verticals. This makes it easier to identify problems, analyze data and make strategic decisions. Furthermore, it reduces operational costs and improves efficiency, ensuring that all sectors of the company operate harmoniously and effectively. However, if developing a monitoring system for your own machines is already a challenge, making it compatible with other types of machines, which use different technologies, is practically impossible and diverts the focus from the main objective of machine manufacturers.
Multiprotocol Communication
To ensure compatibility over time, a monitoring system must support several protocols, facilitating integration between different equipment models and types of communication. Most manufacturers produce their machines using third-party components. In managing these devices, programmable logic controllers (PLCs) are used, which vary in communication protocols and monitoring requirements depending on the required functionalities, their models and brands. Furthermore, some parameters may only be available through additional sensors and, therefore, a well-structured development must include communication protocols for several sensors.
Data Availability
After collecting and processing the information, it is good practice to make the database available to systems complementary to intelligent monitoring. Depending on the client's application and process, customized integrations of the database may be necessary, which should preferably be structured and organic, allowing querying for business analysis systems. This integration process must include a specific area for consultation, such as a datalake, and depending on the implemented architecture of the monitoring system, it can be a significant obstacle to the success of the project.
Embedded Intelligence
A machine monitoring system must incorporate intelligence for fault detection and predictive analysis of the machine. Currently, the use of artificial intelligence is being discussed in several industry sectors. However, building an application, creating a model, calibrating and using intelligence in industrial processes is not trivial and can take months of development, implementation and training of the algorithm, accounting for hours of work and machine processing.
Interoperability
Another need in the area of industrial process monitoring is integration with legacy systems. Depending on the client's application and process, simultaneous processing and sending of collected data to business analysis and management systems may be necessary, possibly sharing with existing systems, such as SCADA systems. Control and Data Acquisition Systems (SCADA) are often used in industrial environments for monitoring and controlling processes. However, these systems are expensive and complex. Integrating them with internally developed monitoring software can be a significant challenge due to differences in architectures, processes and protocols.
Security
Security is a critical aspect in monitoring software development. Own developments can be particularly vulnerable to cyber attacks if they are not properly tested and updated. Protecting software against these threats requires an ongoing effort, including vulnerability and penetration testing by a team independent of development that applies patches and fixes for frequently tested vulnerabilities. Robust security is essential to not only protect system data, but also to ensure customers' trust in the predictive monitoring solution.
Software Maintenance and Update
Developing smart monitoring software is just the beginning. Keeping it up to date and secure is an ongoing and challenging task. With rapid technological evolution; ensure that software remains compatible with new equipment, protocols, browsers and mobile devices; and still maintaining the level of functionality innovation requires a dedicated team of developers. Furthermore, security is a constant concern, as vulnerabilities can be exploited, putting the integrity of data and operations at risk. Effectively maintaining a structured development environment is essential to ensure ongoing performance and customer satisfaction.
Development Cost
Developing smart monitoring software involves a significant initial investment. This cost includes hiring qualified developers, testing and security teams, purchasing development tools, creating a robust testing equipment/systems infrastructure capable of creating a mass of relevant data.
The development process is time-consuming, requires learning and acquiring specific knowledge with careful planning and resource allocation over years.
Efficiency
Efficiency is a crucial factor to be considered when developing monitoring software. The initial investment is significant, and ongoing software maintenance requires a constant allocation of financial and human resources.
Therefore, the return on investment of a proprietary solution is based only on the monitored models of that manufacturer and represents an additional expense with very little chance of return on investment. Evaluating cost-benefit is essential to determine whether developing the monitoring solution in-house is the best approach.
Consolidated Solutions
Faced with these challenges, many machine manufacturers are considering off-the-shelf solutions that offer robust integration and ongoing support. These consolidated solutions not only simplify management, but also provide a holistic and efficient view of the entire infrastructure, ensuring continuity and operational excellence. In order to make the best choice, the manufacturer must compare all the points presented in this article and understand what the solution really offers.
Conclusion
The in-house development of a monitoring and predictive analysis system presents numerous technical and operational challenges for machine manufacturers. The complexity of integrating different equipment, ensuring security, maintaining continuous updates and dealing with high costs makes this task impractical for many companies. Opting for consolidated solutions available on the market, such as Bridgemeter , not only simplifies equipment management, but also guarantees operational efficiency and cost reduction, allowing manufacturers to focus on their core business and offer superior added value to their customers. .
Case Study: Bridgemeter Solution Acquisition
An equipment manufacturer acquired the Bridgemeter remote monitoring solution in a White Label model, transforming the way it manages and monitors its industrial machines and equipment.
Click here to access the full case study and discover how Bridgemeter can revolutionize the management and monitoring of your industrial assets.
