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.

In at least one embodiment, a class 7 or 8 vehicle is provided. A first controller is configured to control a vehicle operation and to detect one or more failures related to the vehicle operation. The first controller is configured to transmit first data indicative of the one or more failures. A vehicle interface controller is configured to receive the first data indicative of the one or more failures on the data communication bus and to receive a signal corresponding to at least one of vehicle speed or park brake status. The vehicle interface controller is further configured to retrieve at least one diagnostic screen and to display the at least one diagnostic screen after the signal indicates the at least one of the vehicle speed being equal to a predetermined vehicle speed or the park brake status indicating that a park brake is set in the vehicle.

An apparatus for identifying a path pattern of devices that produces a defective product in a production line where a product is produced via a plurality of device is provided. The device is configured to estimate a path pattern quality indicating a quality of a group of products produced through a production path included in a path pattern, based on a production path quality and an association relationship between a path pattern and a production path indicating devices via which the product is produced and an order of passing through the devices; and to identify a path pattern suspected to be defective based on the estimated path pattern quality.

Various embodiments include a switching arrangement comprising: two processors; an OR gate; a first position feedback device; and a first switch. The a first switch. The OR gate output is connected to the first switch. The first processor is connected to the first input of the OR gate and the second processor is in operative communication with the OR gate via the second input of the OR gate; At least one of the processors sends a digital ON signal to the OR gate and the OR gate actuates the first switch on receipt thereof. The first position feedback device connects to both processors. The processors are interconnected and each programmed to: read a first position signal from the first position feedback device; send the first position signal to the other processor; compare the read signal to the received signal; and generate an error message if they do not match.

Systems, methods, and circuits utilize one or multiple data-stream watchdog codes for verifying a temporal state of data from a sensor system. A data-stream watchdog system can includes a sensor system configured to detect physical phenomena and produce corresponding output signals; a memory structure configured to store the output signals as sequential data on a repeating cycle, a watchdog code generator configured to insert a watchdog code into the stored sequential data and update the watchdog code periodically, and a data transmitter configured to receive the sequential data with included watchdog code from the memory structure and transmit the sequential data with the included watchdog code over a physical data channel each cycle of the repeating cycle. An application system receives the data and a watchdog code checker checks whether the watchdog code in the sequential data is correct and produces an error indication when the watchdog code is incorrect.

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 method of and a system for detecting a fuel leak in an aircraft, the aircraft comprising a first engine and a second engine. The method comprises upon determining that the aircraft has reached a first mode of operation: acquiring a first baseline fuel flow of the first engine; acquiring a second baseline fuel flow of the second engine. The method further comprises monitoring a first current fuel flow measured at the first engine and a second current fuel flow measured at the second engine; and triggering a fuel leak detection based on an analysis of the first baseline fuel flow, the first current fuel flow, the second baseline fuel flow and the second current fuel flow.

A method and system for detecting and reporting component failures in a linear drive system may identify failed position sensors, failed position magnets, and failed drive coils in the linear drive system. As a mover travels along a track segment in the linear drive system, signals corresponding to the position of the mover and to the current commanded in each drive coil are stored. Analysis of the stored signals identifies whether one of the position sensors along the track segment, one of the position magnets on the movers, or one of the drive coils, used to propel the movers along the track, has failed.

A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.

A plant assistance assessment system 100 that assesses a plurality of nuclear power generation plants 110 includes an abnormality sign monitoring system 121 that monitors an abnormality sign in a plant, based on plant data of the nuclear power generation plants; a fault diagnosis system 122 that performs fault diagnosis on each of devices in the plants, based on abnormality sign detection information; a progress prediction system 123 that simulates progress of the plants, based on fault information; an abnormal phenomenon specification system 124 that specifies a potential abnormal phenomenon in the plants, based on progress prediction information; a risk assessment system 125 that assesses a risk of a fault in the devices in the plants in a probabilistic manner; and an abnormal phenomenon assessment system 126 that assesses likelihood of occurrence of an abnormal phenomenon, based on abnormal phenomenon information and fault probability information.