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 attractive. However, developing in-house monitoring software with predictive analytics presents numerous technical challenges. Let's explore these challenges based on the experience of manufacturers of industrial equipment and machinery.
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 software and hardware to communicate effectively and stably, regardless of the technology type, often through completely different protocols depending on the model. Furthermore, there is a growing demand from customers for unified monitoring, independent of brand or model. System fragmentation, where each type of equipment is managed by a different tool, has proven unfeasible. Managing multiple systems not only increases operational complexity but also hinders obtaining 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 from various application verticals. This facilitates problem identification, data analysis, and strategic decision-making. 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 their own machines is already a challenge, making it compatible with other types of machines using different technologies is practically impossible and diverts attention from the main objective of machine manufacturers.
Multiprotocol Communication
To ensure compatibility over time, a monitoring system must support various protocols, facilitating integration between different equipment models and communication types. Most manufacturers produce their machines using third-party components. Programmable logic controllers (PLCs) are used to manage these devices, and their communication protocols and monitoring requirements vary depending on the required functionalities, models, and brands. Furthermore, some parameters may only be available through additional sensors; therefore, a well-structured development must consider communication protocols for various sensors.
Data Availability
After data collection and processing, it is good practice to make the database available to systems that complement intelligent monitoring. Depending on the client's application and process, customized database integrations may be necessary. Ideally, the database should be structured and organic, allowing queries for business analytics systems. This integration process should include a dedicated query area, such as a data lake, and depending on the implemented architecture of the monitoring system, it can be a significant obstacle to the project's success.
Embedded Intelligence
A machine monitoring system must necessarily incorporate intelligence for fault detection and predictive machine analysis. Currently, the use of artificial intelligence in various industrial sectors is being discussed. However, building an application, creating a model, calibrating and using intelligence in industrial processes are not trivial and can take months of development, deployment 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 application and the client's process, simultaneous processing and transmission of collected data to business analytics and management systems may be necessary, 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 internally 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. Proprietary developments can be particularly vulnerable to cyberattacks if they are not properly tested and updated. Protecting the software against these threats requires an ongoing effort, including vulnerability and penetration testing by a team independent of the development team, which applies patches and fixes for frequently 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 intelligent monitoring software is just the beginning. Keeping it updated and secure is an ongoing and challenging task. With rapid technological evolution, ensuring that the software remains compatible with new equipment, protocols, browsers, and mobile devices, while maintaining the level of innovative features, requires a dedicated team of developers. Furthermore, security is a constant concern, as vulnerabilities can be exploited, jeopardizing data integrity and operations. Effective maintenance of a structured development environment is essential to guarantee its continued performance and customer satisfaction.
Development Cost
Developing intelligent monitoring software involves a significant initial investment. This cost includes hiring qualified developers, a testing and security team, acquiring development tools, and creating a robust equipment/systems infrastructure for testing capable of generating a relevant data set.
The development process is lengthy, requiring learning and the acquisition of specific knowledge through careful planning and the allocation of resources over several 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 the monitored models from that manufacturer and represents an additional expense with very little chance of return on investment. Evaluating the cost-benefit ratio is essential to determine if developing the monitoring solution in-house is the best approach.
Consolidated Solutions
Faced with these challenges, many machine manufacturers are considering readily available market 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. To make the best choice, the manufacturer must compare all the points presented in this article and understand what the solution truly offers.
Conclusion
Developing an in-house monitoring and predictive analytics system presents numerous technical and operational challenges for machine 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 consolidated and commercially available solutions, such as 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 acquired the Bridgemeter remote monitoring solution in a white-label model, 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.
