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Reliable Predictive Maintenance systems
We provide dedicated machine specific software to automatically self-diagnose asset deterioration and impending failure. It is based on algorithms developed over thirty years that are not offered by others.
Our Vision & Mission
Corporate Vision
To be the global leader in the Predictive Maintenance systems (asset diagnostics and prognostics) using a SaaS business model.
Corporate Mission
Develop, manufacture, and market cost attractive software for full time Predictive Maintenance and automated self-diagnostics of industrial and commercial assets.
Our value
Increased Efficiency & Reduced Chances of Storm.
At PredictOS, we specialize in delivering tailor-made, machine-specific software designed to autonomously detect and predict asset deterioration, as well as potential failures. Our cutting-edge technology is built upon three decades of algorithmic development that stands unparalleled in the industry.
Corporate Vision
To be the global leader in the Predictive Maintenance systems (asset diagnostics and prognostics) using a SaaS business model.
Corporate Mission
Develop, manufacture, and market cost attractive software for full time Predictive Maintenance and automated self-diagnostics of industrial and commercial assets.
Who we are
The ultimate software for Predictive Maintenance Systems.
Introducing “PredictOS” – Your Exclusive Source for Tailored Machine-Specific Software Enabling Automated Self-Diagnostics of Asset Decline and Imminent Breakdowns. Our cutting-edge algorithms, honed over three decades, stand unparalleled in the industry, setting us apart from any competition.
Decades of Algorithmic Expertise
Distinguished by algorithms honed over a span of three decades, we stand out from rivals, ensuring precision and dependability.
Paradigm Redefined
Our approach disrupts the conventional Predictive Maintenance paradigm, presenting a novel and enhanced method for superior results.
Applications and Products
Curent Predictive Maintenance Technology
Conventional Predictive Maintenance (PdM) uses vibration analysis to provide early warning to predict imminent failure of machinery to maximize asset uptime, improve
product quality and reduce cost. Each machine has a unique vibration signature based
on its design. PdM is a process and uses analysis charts and graphic patterns like a
heart EKG. A proper analysis of the signature provides a wealth of information. This is
accomplished by installing vibration sensors at key locations on machines to monitor
the condition. The data is analyzed using proprietary software to assess the
machinery’s health.
There is an inherent problem with this process in that it requires an experienced and
costly analyst and considerable time in some instances to diagnose the issues. The
process is based on thirty-year-old technology and is labor intensive.
There are two approaches currently being used. First is periodic monitoring (roughly 85%) using portable data collectors and software for analysis. And the second is online continuous monitoring of critical and important assets (about 15%). The result is trended over time to detect a significant change in asset performance. In the first case, the analysis is based on periodic snapshot of an asset. It is relatively inexpensive but unreliable. For continuous monitoring permanent sensors are installed at key locations. The information is continuously sent to a central server using a proprietary network. Continuous monitoring is obviously advantageous but prohibitively expensive due to the significant cost of sensors. In addition, the current analysis method (algorithms) is mathematically limited and cannot provide self-diagnostics.
There are two approaches currently being used. First is periodic monitoring (roughly 85%) using portable data collectors and software for analysis. And the second is online continuous monitoring of critical and important assets (about 15%). The result is trended over time to detect a significant change in asset performance. In the first case, the analysis is based on periodic snapshot of an asset. It is relatively inexpensive but unreliable. For continuous monitoring permanent sensors are installed at key locations. The information is continuously sent to a central server using a proprietary network. Continuous monitoring is obviously advantageous but prohibitively expensive due to the significant cost of sensors. In addition, the current analysis method (algorithms) is mathematically limited and cannot provide self-diagnostics.
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Listening to you & your Machine, and answering with software
What PredictOS Offers
Unique Self-
Diagnosis
We offer specialized software for automatic asset deterioration and failure prediction, unmatched in the industry.
Decades of Algorithmic Expertise
Our algorithms, refined over three decades, set us apart from competitors, delivering accuracy and reliability.
Advanced Algorithm Mix
PredictOS blends physics-based models, AI-driven machine learning, and asset-specific digital twins for precise analysis.
Powerful Diagnostic Fusion
The software seamlessly integrates motor current signature analysis (MCSA) and vibration assessment for robust diagnostics.
Analyst-Free Operation
Eliminate the need for highly trained analysts; PredictOS empowers all levels of users to benefit from predictive maintenance.
Cost-Efficient Approach
MCSA combined with vibration analysis reduces sensor count, cutting costs and enabling SaaS application.
Paradigm
Redefined
Our methodology challenges the prevailing Predictive Maintenance paradigm, introducing an innovative, more effective approach.
Proven Field Performance
Rigorous beta testing, including simulations and real-world trials, has honed the algorithms, leading to automated diagnostics and bolstered confidence in results.
Who we are
Application Opportunities
Pivotal Role of PdM Tech in Industry 4.0
Amid the push for asset management, digitization, and automated maintenance driven by Industry 4.0, Predictive Maintenance (PdM) technology gains significance. Sensor-monitored assets transmit data to the Cloud, and digital twin models enhance predictive analytics with machine learning, aiming to forecast failure time. Existing solutions fall short in achieving this goal.
Vast Potential of PredictOS
PredictOS technology boasts wide applicability across various standard industrial and commercial machinery, including variable speed assets, compressors, and HVAC systems. This innovation addresses a broad market encompassing data centers, cold storage facilities, hospitals, and large commercial spaces, aligning with the rapidly expanding PdM market.
Get a Trial and Solutions Demo from us
Meet our team
The company has an experienced management team in taking new products from concept to the international market.
Dr. Suri Ganeriwala
C.E.O
Dr. Suri is a globally recognized Subject Matter Expert in machinery diagnostics and signal processing,
Daniel Guest
Software engineer
Daniel Guest has over seven years of experience in software Industry in environmental and medical devices.
James Lowe
Developer
James Lowe has over thirty years of machine design and software development experience.
Dr. Rishi Ganeriwala
Software Engineer
Dr. Rishi Ganeriwala comes with programing experience in developing environmental and medical devices.
Preston Johnson
Sales & Marketing
Preston Johnson has over twenty-five years of experience in sales and marketing of PdM products.
Customer Reviews
(20,702 reviews)
★★★★★ 5/5
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Contact Us
Office Address
8227 Hermitage Road Richmond, VA 23228
Call us
804-261-3300
Fax
804-261-3303
Email us
info@PredictOS.com
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