Technological advancements have led companies across various sectors to incorporate predictive monitoring solutions into their machinery and equipment. The promise of reducing downtime and optimizing maintenance is appealing. However, developing in-house monitoring software with predictive analytics presents numerous technical challenges. We will explore these challenges based on the experience of industrial machinery and equipment manufacturers.
Integration with Equipment and Devices
Establishing secure remote communication with machinery and equipment is one of the greatest technical challenges faced by manufacturers. This integration requires software and hardware to communicate effectively and stably, regardless of the technology used, often using entirely different protocols depending on the model. Furthermore, there is a growing demand from customers for unified monitoring, independent of brand or model. Fragmented systems, where each type of equipment is managed by a separate tool, have proven unfeasible. Managing multiple systems not only increases operational complexity but also makes it difficult to obtain a comprehensive and integrated view of asset performance.
Multisectoral Communication
Customers have become aware that managing multiple systems from different manufacturers is impractical. Increasingly, they demand unified, multi-sector monitoring solutions capable of managing equipment across multiple application verticals. This facilitates problem identification, data analysis, and strategic decision-making. Furthermore, it reduces operating costs and improves efficiency, ensuring that all departments within the company operate smoothly and effectively. However, while developing a monitoring system for their own machines is already a challenge, making it compatible with other types of machines, which utilize different technologies, is virtually impossible and distracts from the machine manufacturers' primary objective.
Multiprotocol Communication
To ensure long-term compatibility, a monitoring system must support multiple protocols, facilitating integration between different equipment models and communication types. Most manufacturers manufacture their machines using third-party components. Programmable logic controllers (PLCs) are used to manage these devices, which vary in communication protocols and monitoring requirements depending on the required functionality, model, and brand. Furthermore, some parameters may only be available through additional sensors, so a well-structured development should consider communication protocols for multiple sensors.
Data Availability
After collecting and processing the information, it's best practice to make the database available to systems complementary to intelligent monitoring. Depending on the client's application and process, custom database integrations may be necessary. These should preferably be structured and organic, allowing queries to be sent to business analytics systems. This integration process should include a dedicated query area, such as a data lake, and depending on the monitoring system's implemented architecture, it can be a significant obstacle to the project's success.
Embedded Intelligence
A machine monitoring system must incorporate intelligence for fault detection and predictive machine analysis. The use of artificial intelligence is currently being discussed in various industrial sectors. However, building an application, creating a model, calibrating it, and applying it to industrial processes are not trivial and can take months of algorithm development, deployment, and training, including hours of labor and machine processing.
Interoperability
Another requirement in industrial process monitoring is integration with legacy systems. Depending on the application and the client's process, simultaneous processing and sending of collected data to business analysis and management systems may be required, and eventually sharing with existing systems, such as SCADA systems. Control and Data Acquisition (SCADA) systems are frequently used in industrial environments for process monitoring and control. However, these systems are expensive and complex. Integrating them with in-house developed monitoring software can be a significant challenge due to differences in architectures, processes, and protocols.
Security
Security is a critical aspect of monitoring software development. In-house developments can be particularly vulnerable to cyberattacks if not properly tested and updated. Protecting software against these threats requires ongoing effort, including vulnerability and penetration testing by a team independent of the development team, who apply patches and fixes for regularly tested vulnerabilities. Robust security is essential to protect not only system data but also to ensure customer confidence in the predictive monitoring solution.
Software Maintenance and Updates
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, ensuring the software remains compatible with new equipment, protocols, browsers, and mobile devices, while maintaining the level of functionality innovation, requires a dedicated team of developers. Furthermore, security is a constant concern, as vulnerabilities can be exploited, jeopardizing the integrity of data and operations. Effectively maintaining a structured development environment is essential to ensure continued performance and customer satisfaction.
Development Cost
Developing smart monitoring software involves a significant initial investment. This cost includes hiring qualified developers, a testing and security team, acquiring development tools, and establishing a robust testing infrastructure capable of generating a significant amount of data.
The development process is time-consuming, requires learning and the acquisition of specific knowledge with careful planning and the allocation of resources over years.
Efficiency
Efficiency is a crucial factor to consider 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 for a proprietary solution is based solely on that manufacturer's monitored models and represents an additional expense with very little chance of return on investment. Evaluating the cost-benefit ratio is essential to determine whether developing the monitoring solution in-house is the best approach.
Consolidated Solutions
Faced with these challenges, many machinery manufacturers are considering off-the-shelf solutions that offer robust integration and ongoing support. These established solutions not only simplify management but also provide a holistic and efficient view of the entire infrastructure, ensuring continuity and operational excellence. To make the best choice, manufacturers should compare all the points presented in this article and understand what the solution truly offers.
Conclusion
Developing a monitoring and predictive analytics system in-house presents numerous technical and operational challenges for machinery manufacturers. The complexity of integrating different equipment, ensuring safety, maintaining continuous updates, and dealing with high costs makes this task impractical for many companies. Opting for established, off-the-shelf solutions like Bridgemeter not only simplifies equipment management but also ensures operational efficiency and cost reduction, allowing manufacturers to focus on their core business and offer superior added value to their customers.
Case Study: Acquisition of the Bridgemeter Solution
An equipment manufacturer has acquired the Bridgemeter White Label remote monitoring solution, transforming the way it manages and monitors its industrial machinery and equipment.
Click here to access the full case study and discover how Bridgemeter can revolutionize the management and monitoring of your industrial assets.
