Abstract: Systems for performing an automated, in situ calibration of one or more sensors of internet of things (IoT) systems include one or more emulators capable of generating calibration set points that are applied to the sensors during the calibration process. The systems also include one or more computing devices configured to store the data necessary for the calibrations. The computing devices are further configured to monitor the sensor outputs during normal operation of the IoT systems to check for a loss of calibration or compromised data integrity; execute an automated calibration of a upon the detection of a loss of calibration or data-integrity issue; and validate the calibration results.

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: Air sensing and purification systems include interconnected components that can both sense and cleanse an airborne viral load quickly and efficiently, minimizing the probability of human infection in the presence of a virus such as COVID-19. The systems can include a biosensing or viral load sensing system configured to measure an airborne viral load and probability of infection from the viral load; and an air purification device configured to effectively neutralize the virus in the adjacent space. The system also can include an alarm hub. The alarm hub can be a local server/hub that is wirelessly connected to the viral load sensing system and the air purification device; and can house strong visual and/or audible alarms to make the user aware of any breach of the social distancing protocol, and/or a high airborne viral load.

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: Systems and processes are provided for managing epidemic and pandemic disease populations by individually scanning persons in a pre-selected retention space, to identify persons having body temperatures in excess of a predetermined level. Such persons are prompted to register, and are instructed to self-quarantine at the address provided during the registration process. The location of the registered person subsequently is tracked to ensure that the person remains at the registered location during the quarantine period. The registration information can be provided to health and law enforcement authorities to help control the spread of the disease population.

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: Systems for performing an automated, in situ calibration of one or more sensors of internet of things (IoT) systems include one or more emulators capable of generating calibration set points that are applied to the sensors during the calibration process. The systems also include one or more computing devices configured to store the data necessary for the calibrations. The computing devices are further configured to monitor the sensor outputs during normal operation of the IoT systems to check for a loss of calibration or compromised data integrity; execute an automated calibration of a upon the detection of a loss of calibration or data-integrity issue; and validate the calibration results.

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.

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. 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.

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.