Working Against Unplanned Downtime
The fusion of machines’ properties and sensor data through AI algorithms allows for accurate prediction of the future behavior of machines, enabling proactive maintenance and reducing downtime. This results in cost savings, increased productivity, and improved safety.
Root Cause Analysis
Anomaly detection triggers root cause classification algorithms to conduct proper maintenance actions.
From Day One
State of art analysis techniques start with the first measurement and then get better day by day.
Adapts to Boutique Machinery
Data integration abilities grant successful predictions on complex mechanisms and variable processes.
Robust AI, Effortless Knowledge
Sensemore AI refines all the power behind its models into intuitive and detailed reports for our users to focus on only the necessary actions.
An Overview on Machine Health
AI generated reports can give a general overview on your system health or detailed information on a specific machinery.
Continuously Developing
Sensemore focuses on improving its AI features day by day using state of the art methods. You won't miss any updates.
Remaining Useful Lifetime Estimation
With Sensemore AI, gradual faults can be detected in their earliest stages and their advancement can be predicted by creating remaining useful lifetime estimation from the earliest stage possible.
A Reliable Assistant
Sensemore AI solutions operate and watch over your machinery 24/7 not missing any beat, letting you plan your downtime, and ensuring smooth operation. Sensemore AI will become your most reliable assistant.
Reliability Agent
Sensemore Reliability Agent works the case inside LAKE and comes back with a diagnosis, the evidence behind it and a proposed action.
Only Gets Better
Our AI framework supplies a complete toolbox learning from the past and forecasting the future.
Machine Mode Analysis
Simplifies thousands of measurements by learning your machine characteristics, detecting patterns, and identifying anomalies. Focus on only the most important!
What a Machine Mode Model Shows
A model is built from the machine’s own recent measurements. When it is ready it names the states the machine actually runs in, how much of its time it spends in each, and what each one looks like, so a change in behaviour becomes something you can see rather than something you have to go looking for.
Operating Modes
How many states the machine runs in and which one dominates. For each: its share of the measurements, its typical running speed and vibration level, and the days it appeared. Modes take the names your team already uses.
Spectral Fingerprints
A representative three-axis spectrum for every mode, taken from a real measurement, with running speed marked and the split of energy across the axes shown beside it.
Matched or Flagged
New measurements are matched to the mode they belong to. One that matches nothing is flagged for review rather than forced into a mode, and the modes that matter to you can raise a notification.
Root Cause Analysis
Pinpoints the underlying issues and provides detailed insights in order for you to take efficient and effective maintenance actions.
Machine Learning
Gets Stronger with You
With the knowledge of our reliability engineers and artificial intelligence along with incorporating valuable customer feedback, Sensemore AI offers powerful and robust solutions for smart machine health monitoring, delivering improved performance, and unparalleled customer satisfaction.
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