Machinesense, LLC

Prophecy Sensorlytics, LLC

Biplab Pal

Steve Gillmeister

Amit Purohit

James Zinski

Steven Gillmeister

Soumya Boral

Amit Chakrabarty

Pratyay Karar

Neeraj Nagi

Anshul Bansal

Utpal Manna

Sneha Dutta

Abhisek Dey

Maniruz Zaman

A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Granular material dryer for process of resin material prior to molding or extrusion

Dryer machine learning predictive maintenance method and apparatus

A method and system of detecting faults in rotor driven equipment includes generating data from one or more vibration sensors communicatively coupled to the rotor driven equipment. The data from the one or more machine wearable sensors is collected onto a mobile data collector. The data is sampled at random to estimate a maximum value. Further, a sampling error may be controlled under a predefined value. The data may be analyzed through a combination of Cartesian to Spherical transformation, statistics of the entity extraction (such as variance of azimuthal angle), big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.

Detecting faults in rotor driven equipment

A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Internet of things based conveyance having predictive maintenance

A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Real time machine learning based predictive and preventive maintenance of vacuum pump

A method for accurately measuring the real-time valid dew-point value of a material and determining the total moisture content of the material by using an algorithm during the material drying process. The algorithm estimates the valid dew-point value of the material and the total moisture content of the material by analyzing sensor data received on a server. The algorithm determines a valid dew-point value by estimating an inflection point of the moisture content versus time friction/curve for the material, and the total moisture content of the material is determined within the valid dew-point value.

Methods for measuring real-time dew-point value and total moisture content of material to be molded or extruded

Predicting maintenance needs and analyzing preventative maintenance requirements in electrically powered turbomachinery with multi-parameter sensors and power quality sensors, both of the Fog-type, providing time domain output data and transforming data samples into the frequency domain to detect a root cause of failure of the machinery.

Preventive maintenance and failure cause determinations in turbomachinery

A method of evaluating factory production machinery up time and down time performance provides a collection of sensors in individual communication with factory production machinery, with each sensor collecting high frequency vector data as respecting a physical parameter associated with the machinery, extracts the data from the sensors to produce a sensor data set, transforms the data set into the frequency domain, extracts statistical and mathematical information from the data set, transfers the data set, to an associated edge cloud, and within the associated edge cloud processes the data set to provide a repair, maintenance and operation board for the machinery to evaluate up time and down time performance for the factory production machinery.

Edge cloud-based resin material drying system and method

Machine learning based predictive maintenance of a dryer

System and method for turbomachinery preventive maintenance and root cause failure determination

A system and method for accurately measuring the real-time valid dew-point value of a material and determining the total moisture content of the material within the valid dew-point value by using an algorithm during the material drying process. The algorithm estimates the valid dew-point value of the material and the total moisture content of the material by analyzing the sensor data received on a server. The algorithm determines a valid dew-point value by estimating an inflection point for the material, and the total moisture content of the material is determined within the valid dew-point value.

Method for accurately measuring real-time dew-point value and total moisture content of a material

A computer implemented method and system for determining reliability of a machine includes receiving one of a machine data from one or more locations through an internet of things (IOT) based machine wearable sensor network. The method further includes storing the data in a distributed computer database communicatively coupled to an enterprise resource planning (ERP) system and extracting, through a computer server, one or more entity information from the data to compare against a pre-defined baseline. Further, mapping, though a big data machine learning engine, the extracted one or more entity information into a multi-classification model. The method includes indicating, through a machine learning engine coupled to a predictive analytics engine, on a user interface a set of analytical predictions for machine maintenance, repair and operation.

Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs

Method and apparatus for pneumatically conveying particulate material including a user-visible IoT-based classification and predictive maintenance system noting maintenance state as being acceptable, cautionary, or dangerous

A method and system of a distributed power line diagnosis includes one or more electrical readings received at a firmware board computation engine and an output of firmware board engine computation being transmitted through a wide communication network to a data hub computation engine. An output of the data hub computation engine is transmitted through the communication network to a big data server. One or more electrical line issues are visualized based on an analysis through the big data server and the same can be indicated through a user interface dynamic and an alarm can be set as well through the processor for the one or more electrical line issues.

Distributed IoT based sensor analytics for power line diagnosis

An automated earth fault testing system and early warning system designed to be used with mobile towers for real-time monitoring of the earthing values. The automated earth fault testing system comprises an earth fault testing device powered by a low voltage direct current battery, a plurality of terminals, at least one calibration switch including a calibration pad for calibrating the earth fault testing device, a plurality of visual indication means for providing indication of a variety of conditions including high and/or normal value of the earth resistance value and for indicating a charge level of the earth fault testing device and a liquid crystal display for displaying the earthing values. The earth fault testing device is connected to an alarm system of a base transceiver station utilizing a relay for informing the mobile signal station and operator with information regarding a status of the earth fault testing device.

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Ellicott City, MD, United States of America

Biplab Pal