Technological advancement has led companies from various sectors to incorporate predictive monitoring solutions into their machines and equipment. The promise of reducing inactivity time and optimizing maintenance is attractive. However, the internal development of predictive monitoring software presents numerous technical challenges. Let's explore these challenges based on the experience of industrial equipment and machine manufacturers.
Integration with equipment and devices
Establishing a safe 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 stablely, regardless of technology type, often through totally different protocols, according to the model. In addition, there is a growing demand from customers for unified monitoring, which is independent of the brand or model. Systems fragmentation, where each type of equipment is managed by a distinct tool, was unfeasible. Management of 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 already become aware that it is impractical to manage various systems from different manufacturers. Increasingly, they demand unified multisectoral monitoring solutions, capable of managing equipment from various application verticals. This facilitates problem identification, data analysis and strategic decision making. In addition, it reduces operating costs and improves efficiency, ensuring that all sectors of the company operate in a harmonious and effective manner. However, 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 virtually impossible and diverts the focus of 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 types of communication. Most manufacturers produce their machines using third party components. In managing these devices, they are programmable logic controlling employees (PLCs), which vary in communication protocols and monitoring requirements depending on the required features, their models and brands. In addition, some parameters may be available only through additional sensors and, therefore, well -structured development should include communication protocols from various sensors.
Data availability
After collecting and processing information, it is good to make the database available for complementary systems for intelligent monitoring. Depending on the application and process of the customer, custom database integrations may be required that should preferably be structured and organic, allowing consultation for business analysis systems. This integration process should include an area of its own for the consultation, such as a Datalake, and depending on the implemented architecture of the monitoring system, can be a significant obstacle to project success.
Embedded intelligence
A machine monitoring system must necessarily incorporate intelligence for fault detection and predictive machine analysis. Currently, the use of artificial intelligence is discussed in various sectors of the industry. However, the construction of an application, the creation of a model, the calibration and use of intelligence in industrial processes are not trivial and can take months of development, implementation and training of the algorithm, accounting for work hours and machine processing.
Interoperability
Another need in the area of industrial process monitoring is integration with legacy systems. Depending on the application and process of the customer, simultaneous processes and shipments of the data collected to business analysis systems may be required, eventually sharing with existing systems, such as SCADA systems. Data Control and Acquisition Systems (SCADA) are often 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 in the development of monitoring software. Own developments can be particularly vulnerable to cyber attacks if not properly tested and updated. Protecting the software against these threats requires continuous effort, including vulnerability tests and penetration by an independent development team, which apply patches and corrections for frequently tested vulnerabilities. Roil security is essential to protect not only system data, but also to ensure customer confidence in predictive monitoring solution.
Software maintenance and updating
Developing smart monitoring software is just the beginning. Keeping it up to date and safe is a continuous and challenging task. With the rapid technological evolution; Ensure that the software remains compatible with new equipment, protocols, browsers and mobile devices; And maintaining the level of innovation of functionality, requires a dedicated team of developers. In addition, security is a constant concern, as vulnerabilities can be explored, endangering data and operations integrity. Effective maintenance of a structured development environment is essential to ensure continuous performance and customer satisfaction.
Development cost
The development of intelligent monitoring software involves a significant initial investment. This cost includes hiring qualified developers, test and safety team, acquisition of development tools, creating a robust equipment/systems infrastructure for testing that can create a relevant data mass.
The development process is time consuming, requires learning and acquisition of specific knowledge with careful planning and resource allocation over years.
Efficiency
Efficiency is a crucial factor to be considered in developing monitoring software. The initial investment is significant, and continuous maintenance of the software requires a constant allocation of financial and human resources.
Therefore, the return on investment of a pro -regular solution is based only on the monitored models of that manufacturer and represents an additional expense with very little chance of return on investment. Assessing cost-benefit ratio is essential to determine whether internal monitoring solution is the best approach.
Consolidated solutions
Given these challenges, many machine manufacturers are considering market solutions already available, which offer robust integration and continuous support. These solutions consolidated 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 points presented in this article and understand what the solution really offers.
Conclusion
The internal development of a predictive monitoring and analysis 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. Opt for consolidated and available solutions on the market, 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 higher added value to their customers. .
Case Study: Acquisition of the Bridgemeter solution
A equipment manufacturer acquired the Bridgemeter Remote Monitoring solution into White Label model, transforming the way it manages and monitors its industrial machinery and equipment.
Click here to access the full case study and find out how Bridgemeter can revolutionize the management and monitoring of its industrial assets.
