The fourth industrial revolution sees the affirmation of a new paradigm: competitive advantage is now based on data, as a source of information and driver of competitive advantage. The progressive multiplication of connected devices, thanks to IoT technologies, able to communicate with each other and with operators, has given rise to the affirm of the modern “Big Data” trend in the industrial sector: the machines are now able to produce huge quantities of data, with interesting consequences on management.
The three pillars of the Big Data phenomenon, that are monitoring, analysis and predictability, require the adoption of integrated solutions in order to streamline operations and optimize processes.
Our solution for the monitoring of machine tools comes from the combination of a cutting-edge technological platform and our specialized know-how, gained through years of experience serving the needs of our customers.


In this context, Sentinel is positioned as an integrated solution for monitoring machine tools.
Our advanced manufacturing solution enriches the technological content of metal and stone processing machines, making them intelligent: the organic nature of the solution allows for the most diverse applications, from monitoring the status of the machine and its performance to incremental levels of detail ( up to the level of individual tools), ordinary maintenance and predictive maintenance applications. The interaction is guaranteed by alarms and push notifications when anomalies or over / under thresholds occur, with the possibility of integrating with platforms like Twitter through “social machine” mechanisms.
The solution consists of a hardware part, a “black-box” on the machine, equipped with sophisticated sensors and modular soft-PLCs, and a software part, characterized by a scalable and highly customizable platform, based on Microsoft technology.


Sentinel allows to monitor in real-time the status of the machine or plant from PC, tablet and smartphone. The solution allows all personnel, from the operator to the production manager, to keep the operating and performance parameters under control.
• Immediate visual alert on the state of health of the machine with the possibility to activate push notifications
• Possibility of connecting to social platforms such as Twitter to trigger collaboration on the data
• Basic metrics such as hours worked, pieces produced, square meters cut, detailed by shift, material order
• Calculation of the OEE through integration with machine availability data


Correct use and punctual monitoring of the spindle makes the difference on the overall performance of the machine. The evaluation of the main parameters, such as the speed or the temperature of a spindle, occurs through the use of advanced sensors. Data can be filtered according to different levels of detail, based on the objectives of the end user:
• Operational analysis of the single spindle
• Comparison of the actual working points with theoretical usage curve, projection on the actual consumption of the spindle’s expected life
• Data export related to the use of a spindle in a given time interval


Possibility of managing ordinary maintenance in a completely integrated way with the real operation of the machine. The operator is constantly supported by the platform, with the possibility of planning maintenance and obtaining instructions and related materials.
• Maintenance can be set on a time basis, but also on the occurrence of certain conditions (for example exceeding thresholds)
• Sentinel saves and manages all the machine documentation provided by the manufacturer, warning in advance of the maintenance deadline
• The user can add other customized contents related to maintenance operations (photos, videos, control reports)
• The platform traces the history of maintenance performed, allowing for comparison and analysis of trends


Machine’s operating data constitute a real “treasure trove” from which to extract value, not only to interpret the past, but also to know the future. By acting on the main variables, applying appropriate algorithms, to investigate the occurrence of anomalies in order to implement preventive corrective actions.


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