Our system is designed to solve a several problems:
- Environmental damage control – By preventing oil & gas and water leaks or waste.
- Efficiency – We will prevent an IDLE time of the machinery and pipelines by reducing false alarms by 85% guaranteed. Furthermore with us you won’t need to schedule your maintenance, you will actually enjoy predictive maintenance. We will save you a lot of money, and meanwhile help you keep the world a better place to live in.
The diagnostic system is based on a statistical algorithm that processes the data from the sensors installed on the diagnosed mechanical part.
At the time the algorithm receives the data from sensors, it calculates the “system health grade”, makes a decision on normality of the system and outputs the alerts if needed.
These alerts can be delivered to the equipment operators in any form, according to the clients requirements.
What are we proud of?
DiagSense has developed a state of the art statistical algorithm designed to detect evolving malfunctions in mechanical parts/systems and pipelines. Our prediction is the most accurate existing today in the market thanks to our unique algorithm. Our solution will save you time and Money; we are willing to demonstrate our solution free of charge on data collected from your mechanical system or pipeline.
Any mechanical system or pipeline infrastructure with a monitoring system (or without – our system can be an SW add on or stand-alone system) that has sensors installed on it is a DiagSense potential customer. We choose to focus on the pipeline infrastructures – Gas, Oil & Water. These markets have a great need for good solid and dependable failure prediction systems that will help them save hundreds of millions of dollars every year – by preventing environmental damage, waste and leaks.
Why are we better?
- Our system works Real-time 24/7 with no need for calibration after any changes of state.
- Our competitors examine only the failure signal; we examine the influence of various external parameters and the correlation between them.
- Our competitors use a large DB of previous malfunctions of similar devices; we are looking for deviation from normal behavior of the same device.
- We consider external signals, offering us the ability to decrease significantly false alarms compared to our competitors.
- Our learning is statistical not physical. This enables us to “smooth” the noises and undesired/sudden events.
- Eventually, Diagnosed solution has no mis-detection, and at the other hand we dramatically reduce false alarms compared to any other competitors.
Our financial model is based on three main elements.
- Set up of a system.
- Monthly maintenance and support retainer (varies from one customer to the other, depending on the size of the system, needs and etc.)
- SW License.