Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for monitoring the condition of structural systems such as bridges and roadbeds. The systems include a magnetometer mounted on a structural element of the structural system; and a magnet mounted on a surface adjacent the structural element so that the magnetometer is positioned within a magnetic field of the magnet. The magnetometer measures characteristics of the magnetic field of the magnet. Position of the structural element is determined from measured characteristics of the magnetic field and a predetermined relationship between the characteristics of the magnetic field and the position of the structural element within the magnetic field. The position information determines other parameters, such as the deflection of the structural element in three-dimensional space, and the response of the structural element to dynamic loading.

Abstract: 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.

Abstract: 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.

Abstract: System and apparatus for monitoring a structural element includes a magnetometer capable of being mounted on the structural element, a magnet capable of being mounted on a surface adjacent the structural element so that the magnetometer is positioned within a magnetic field of the magnet; and a computing device capable of being communicatively coupled to the magnetometer, the magnetometer measuring characteristics of the magnetic field of the magnet, the computing device determining deflection of the structural element based on the measured characteristics of the magnetic field and a mathematical relationship between characteristics of the magnetic field and position of the magnetometer in relation to the magnet.

Abstract: A field learning system comprising a system of feedback using a user interface in a web based and mobile application to overcome the difficulty and infeasibility of supervised machine learning systems used for modeling failure states of machines.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Apparatus, system, and method for diagnosing status of electrical line performance by receiving and analyzing a plurality of electrical line data from a plurality of lines includes a collection of internet of things sensors, a communication network, local firmware boards, a data hub, a computation engine, a machine learning engine, and an internet of things server operatively connected to the machine learning engine.

Abstract: 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.

Abstract: A system for rule management, predictive maintenance and quality assurance of a process using automatic rule formation comprising a plurality of sensors capable of being attached to at least one machine for measuring at least one information about the process and machine operation. The system comprises a server connected to the sensors over a wireless communication network and running a reconfigurable rule management program for identifying and processing the particular process and machine information related to at least one process received from the plurality of sensors. A controller in communication with the server capable of controlling the process based on a rule set by the rule engine. The rule engine automatically detects the normal process data, classifies the received data based on the dynamic rule formed by the rule engine and finds anomalies in the process or machine operation for predictive maintenance and process quality assurance.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.