Data analysis has become more advanced
Today’s integration of machines and engines with service and operators has made data analysis indispensable for engine reliability. Within the industry of the non-road sector there have been major changes in technology innovations of sensor technology and data systems. This has resulted in a transformative shift in the integration of remote data. Advances in semiconductor and nano technology, have enabled data analysis to be performed more digitally.
Initially the market relied on manual diagnostics and on-site monitoring, consuming many service hours and downtime of the application. Maintenance routines were based on fixed schedules, meaning that after a predetermined number of operating hours or given period, the engine would need to undergo maintenance. This approach often led to either inefficient maintenance or missed issues. Now, remote data provides real-time insights in the data of the engine and capture this data for analysing engine performance over a period of time.
Remote data systems operations
An Engine Management System (EMS) is a mixed-signal embedded system, interacting with the engine through a number of sensors and actuators. A combination of sensors and Electronic Control Units (ECU’s) continuously monitors the performance of the engine, such as temperature, oil pressure, fuel consumption, emissions and RPM. As these sensors generate a constant flow of raw data, they are collected by an on-board telemetry system. This unit acts as a central hub, collecting the data and preparing it for transmission to the cloud.


The method of transmission of the data to the cloud depends on the location and the application. Different solutions can be employed for the transmission of data, like satellite communication, cellular networks or Wi-Fi connections. To ensure data security and integrity during transmission, remote data systems employ encryption protocols and error-checking algorithms, minimizing the risk of data loss or tampering.
The raw data is processed and analyzed in the cloud, using advanced algorithms. These algorithms clean up, structure and interpret the information, extracting valuable insights and flagging potential issues. The processed data will be presented to end-users in an App with customizable dashboards displaying real-time metrics, historical trends and predictive maintenance alerts. This then provides a comprehensive overview of the engine performance.
Project: Implementing IODA to Zenoro marine generators
Driven by the need for real-time data visibility and precise service capabilities, we initiated the implementation of IODA Scout across all Zenoro marine generators. IODA Scout captures live operational data, including RPM, key technical parameters, and performance metrics, enabling both real-time monitoring and advanced diagnostics.


The system opens up opportunities for predictive maintenance, allowing us to proactively address issues and optimize generator performance. Additionally, it provides valuable insights that can help identify areas for future improvement.
While the backend system is still under development, we are already collecting historical data, being used to analyze engine behaviour across different applications. This ongoing data collection helps refine predictive models and enhances our understanding of usage patterns.
The launch of our internal Zenoro Coach App is further enhancing monitoring capabilities, allowing users to track various data points and make necessary adjustments in real time. With the integration of a dedicated dashboard, field testing has been streamlined, providing a practical platform for ongoing evaluation and optimization of the remote data system.
The future of remote data
Looking forward, the role of remote data in non-road applications is set to expand further as it continues to develop. With the new developments in machine learning and AI, remote monitoring will become more sophisticated and capable of autonomous diagnostics. By continuously monitoring real-time operational data, service intervals can be optimized based on the actual engine usage.
These advancements will provide a deeper insight into how faults develop, allowing issues to be quickly identified. Using historical data and real-time monitoring, potential issues can be detected early and resolved before they escalate. Additionally, machine learning will further enhance the capabilities of remote data. This can be done through analyzing major datasets to identify patterns and predict engine behaviour. These algorithms will make automated adjustments, improving efficiency and reliability without manual intervention.
Overall, the integration of advanced remote data solutions will definitely transform engine management.

A Transformation in Engine Maintenance
“The integration of remote data into the world of engines marks a true transformation in how we monitor performance and maintenance. Where we once relied on manual diagnostics and fixed maintenance schedules, we can now rely on real-time data analysis to identify issues more quickly and to plan proactive maintenance. For me, it’s fascinating to see how the integration of remote data has completely changed our approach to engine management. Every day, I work on optimizing our data output while simultaneously developing customized systems tailored to the needs of our customers. What excites me most is that this technology not only enhances engine reliability but also elevates the customer experience to a whole new level.”