Oil & Gas

Background Drilling Noise:

The constant background drilling noise, particularly around the Top Drive, adds an extra layer of complexity to monitoring equipment health. Precise sensor placement and advanced noise-filtering algorithms are crucial to extracting meaningful data in this environment.

Short Periods of Consistent Rotation (Drawworks):

The drawworks’ short periods of consistent rotation require specialized monitoring techniques to capture rapid changes in equipment conditions. Predictive maintenance solutions must be tailored to handle these dynamic operational patterns.

Hazardous Area Locations:

Oil & Gas manufacturing and production platforms often have hazardous areas where the atmosphere contains, or may contain in sufficient quantities, flammable or explosive gases, dust, or vapours. A slight spark, for example, in an oil and gas field or a manufacturing plant could trigger a fire or an explosion bringing about equipment damage or worse, loss of life. Operating in such locations necessitates certified hardware, ensuring compliance with safety standards while maintaining reliable data collection and anomaly detection.

Oil and Gas Predictive Maintenance Solutions with Nanoprecise

Cellular Communication with eSim

MachineDoctor sensors utilize cellular communication with eSim technology. This advanced communication approach ensures secure, reliable, and interference-free data transmission. With eSim, we eliminate the risk of interference, providing you with real-time, accurate insights into your equipment’s health, regardless of the complexities of your operational environment.

Cybersecure Software

 Enhance your plant’s visibility effortlessly with our Plug & Play Sensors that facilitate swift installation, and seamless connectivity to Cellular (2g/4g/NbIoT) or Wi-Fi in less than 5 minutes—and benefit from immediate anomaly detection right from day one.

Adaptive Learning

Our AI-based platform can be set up in as little as 5 days, rapidly learning and creating a range-bounded baseline for each machine’s performance. This adaptive learning process is suitable for monitoring variable speed equipment and capturing rapid changes in the operating conditions of various equipment sets. It enables the platform to understand and adapt to your actual operations over time, refining its understanding and creating adaptive thresholds with defined range boundaries. This dynamic adaptation ensures that our predictive maintenance solution remains finely tuned to your specific conditions, enhancing accuracy, and reducing false alarms.

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Oil and Gas Predictive Maintenance Case Study

Catastrophic Failure Avoided in a 4000hp Compressor Through Early Fault Detection

A major industrial plant faced the risk of catastrophic compressor failure due to undetected mechanical instability. Operating without predictive insights led to reactive maintenance and significant operational risk.

To mitigate this, Nanoprecise deployed our RotationLF system integrated with MachineDoctorâ„¢ wireless sensors on key assets during a pilot project. These sensors transmitted real-time data to our secure, cloud-based platform, where AI algorithms continuously analyzed asset health.

Just 15 days post-installation, early-stage fault patterns—specifically 1x harmonic frequency variations—were identified on a 4000hp compressor. These fluctuations indicated load instability caused by compromised oil film integrity. The maintenance team was immediately alerted via email.

By acting early, the team was able to schedule maintenance before any major damage occurred—preventing a potential catastrophic failure and unplanned downtime, and restoring vibration levels to normal.

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