An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit, which simultaneously outputs, as sensing data, pieces of detection data respectively output from the plurality of sensors on the basis of the sensing period of any one of the plurality of sensors, determines whether each region of an outer cover corresponding to a location of each of the plurality of sensors is contaminated on the basis of the pieces of detection data, and outputs a determination result as contamination data.

A substrate processing system includes: an acquiring unit configured to acquire process data of each step when each step included in a predetermined process is executed under different control conditions; an extracting unit configured to divide each step into a first section in which the process data fluctuates and a second section in which the process data is converged, and extract first data belonging to the first section and second data belonging to the second section from the process data; and a monitoring unit configured to monitor the process data by comparing one or both of an evaluation value that evaluates the first data and an evaluation value that evaluates the second data with corresponding upper and lower limit values.

Examples disclosed herein are directed to a method and apparatus for determining a position of a ring within a process kit. In one example, a sensor assembly for a substrate processing chamber is provided. The sensor assembly includes a housing having a top surface, a bottom surface opposite the top surface, and a plurality of sidewalls connecting the top surface to the bottom surface. The housing also has a recess in the top surface, the recess forming an interior volume within the housing. The sensory assembly includes a bias member, and a contact member disposed on the bias member. The bias member and contact member are disposed within the recess. A sensor is configured to detect a displacement of the contact member. The displacement of the contact member corresponds to a relative position of an edge ring.

The present invention is directed to a device capable of implementing a machine learning algorithm to identify states of operation, performance, and health of a piece of machinery based on vibration and sound patterns. The present invention features a small electronic device consisting of one or more sensors with a computing device, that collects patterns of vibration and/or acoustic measurement from machinery to generate one or more representative signals. The device uses a simple algorithm to characterize the representative signals, and uses a simple algorithm to compare future signals to the characterized signals.

An apparatus, method, and non-transitory machine-readable medium provide for 360° assistance for a QCS scanner with mixed reality (MR) and machine learning technology. The apparatus includes an optical sensor, a display, a Chatbot, cloud service, and a processor operably connected to the optical sensor and the display. The processor receives diagnostic information from a server related to a field device in an industrial process control and automation system; identifies an issue of the field device based on the diagnostic information; detects, using the optical sensor, the field device corresponding to the identified issue; guides, using the display, a user to a location and a scanner part of the field device that is related to the issue; provides, using the display, necessary steps or actions to resolve the issue; and connects, using a cloud server, a user to get modules of installation, commissioning, annual maintenance (AMC) and training for a quality control system (QCS) as per the selected persona.

An opt-in from at least one user of a plurality of users associated with at least one tool of a plurality of tools is received. An authentication associated with a first opted-in user of the plurality of users associated with an access of a first tool of the plurality of tools is determined. A set of credentials required to operate the first tool associated with the first opted-in user is verified. A request to an Internet of things (IoT) receiver device is transmitted. A response from an IoT transmitter device is received. In response to determining that the first user is utilizing required equipment to operate the first tool, power to the first tool is supplied.

A cyber-physical system may have monitoring nodes that generate a series of current monitoring node values over time that represent current operation of the system. A hierarchical abnormality localization computer platform accesses a multi-level hierarchy of elements, and elements in a first level of the hierarchy are associated with elements in at least one lower level of the hierarchy and at least some elements may be associated with monitoring nodes. The computer platform may then determine, based on feature vectors and a decision boundary, an abnormality status for a first element in the highest level of the hierarchy. If the abnormality status indicates an abnormality, the computer platform may determine an abnormality status for elements, associated with the first element, in at least one level of the hierarchy lower than the level of the first element. These determinations may be repeated until an abnormality is localized to a monitoring node.

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as ‘process parameters’) and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

Provided is a monitoring apparatus comprising: an association acquisition unit configured to acquire, for each of a plurality of pieces of equipment, an association with at least one sensor monitoring a state of the each of the plurality of pieces of equipment; a calculation unit configured to calculate, based on at least one measurement value from the at least one sensor associated with each piece of equipment of the plurality of pieces of equipment, a state value of each piece of equipment; a ranking unit configured to rank the plurality of pieces of equipment according to a change width or a change rate in the state value; and a display processing unit configured to perform a processing to display an information screen corresponding to the ranking of the plurality of pieces of equipment.

A wellbore pumping unit comprising a main pump, a power source, a controller, and a plurality of sensors is provided. A method of performing a diagnostic check of the unit controller and the plurality of sensors when power is applied to the unit controller. A diagnostic application executing in memory writes a status of the plurality of analog inputs of a plurality of valve position sensors, a status of a plurality of frequency inputs of a plurality of flowmeters, and a plurality of calibration data files that correlate to a plurality of sensors to a diagnostic report file. The status of the sensor inputs and calibration data files can be a pass or a fail. The status of the sensor inputs can be displayed on a human machine interface.