The Challenge:

Large machinery and drilling equipment are key components in the context of Oil & Gas Industry operations. Employment of human personnel is often required in order to monitor these assets and to guarantee their continuous operation.


This generates high economical and liability exposure, since the requirements of this labour of supervision make it so the workforce needs to operate in close proximity to heavy machinery.

The Solution:

Insight-AI can assess a wide range of standard and tailored indexes of normal and paranormative machinery operativity, whose evaluation has been traditionally delegated to specialized human labour. By providing a video based layer of machinery supervision metrics, Insight-AI keeps pushing forward the automation process.

Complex information can complement automatic neural network based object detection, such as metadata extracted from thermal imaging video streams and spectrograms from audio streams.

The Technology:

Insight-AI neural networks are built and deployed in modular fashion, so as to be easily adapted and customized to a wide range of equipment and scenarios. Example of variables that are indexed by Insight-AI include:

Machinery temperature: by means of thermal cameras, it is possible to trigger alerts either according to thermal peaks thresholds or to paranormative patterns thermal patterns.

Motion pattern detection: visual cues from equipment such as speed, trajectory, vibration, and dislocation can be easily operationalized in realtime, and associated to alarm thresholds.

Damage detection: visual signs of wear and damage such as cracks, rust progression, leakage, electric sparks, and smoke presence can be detected and associated to alarm triggers.

Paranormative sound patterns: by analyzing in real-time the audio spectrograms of the noise produced by single machines, it is possible to identify patterns that match specific operational failures and predict and prevent machinery corruption.


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