The embodiments of the present disclosure provide a fault diagnosis method, a method for building a fault diagnosis model, fault diagnosis equipment, electronic device, and non-transitory computer-readable storage medium. The fault diagnosis method, for diagnosing a fluid device, which includes a suction end and a discharge end, includes: obtaining a data set for diagnosing the fluid device, wherein the data set includes first characteristic data about the suction end, second characteristic data about the discharge end, and input-output difference data, and the input-output difference data represents data difference between the suction end and the discharge end; obtaining a fault diagnosis model; and determining whether the fluid device is in failure based on the fault diagnosis model and the data set.

An information processing device includes: a memory; and a processor coupled to the memory and configured to: learn a classification rule that classifies an abnormal degree of a production facility from a text feature amount based on the text feature amount obtained from a number of texts included in a plurality of pieces of log data obtained in a predetermined process of the production facility and production history information of the production facility; extract a text feature amount of log data to be monitored obtained in the predetermined process of the production facility; and determine an abnormal degree of the production facility when the log data to be monitored is obtained based on the text feature amount and the classification rule.

A system of digitally representing equipment failure comprising: a testing assembly for testing a component of a manufacturing equipment wherein the component includes a defect in a critical area included in the component; a sensor in communications with the testing assembly for sensing a failure state of the component, a set of computer readable instructions adapted for: receiving a critical failure mode associated with the component, receiving a testing dataset from the sensor representing testing results produced by the testing assembly wherein the testing dataset includes initial data representing an undamaged component and a failure dataset representing a failed component, isolating a set of failure data representing a testing status of the component from initial testing to failure of the component determined by the critical failure mode, creating a usable lifetime model of the component according to the set of failure data.

A hot water supply device including an inlet pipe, an outlet pipe, a burner unit, a heat exchanger, an exhaust aperture, a first temperature sensor detecting a measured exhaust temperature of the exhaust gas, a second temperature sensor detecting a water temperature of water entering the inlet pipe, and a processor. The processor is configured to obtain an error between the measured exhaust temperature and an estimated exhaust temperature, and detects that scale clogging has occurred inside the heat exchange tubing based on an index which is generated using the error between the measured exhaust temperature and the estimated exhaust temperature. The estimated exhaust temperature is a first predetermined value that is determined using a numerical equation which has at least the water temperature of water entering the inlet pipe and a scale number of the hot water supply device as variables of the numerical equation.

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

A computer-implemented method, system and computer program product for detecting or predicting system faults in cooling systems. A model (deep learning model) is built and trained to detect or predict system faults in a cooling system based on acoustic emission signals (both in temporal and frequency domains) and/or imaging signals. Upon training the model to detect or predict system faults in a cooling system, acoustic emission signals may be obtained non-intrusively from the cooling system using acoustic emission sensors, hydrophones and/or microphones. Additionally, upon training the model to detect or predict system faults in a cooling system, imaging signals (e.g., boiling images) may be obtained non-intrusively from the cooling system using optical sensors (e.g., high-speed camera). The trained model may then detect or predict a system fault in the cooling system based on such information (acoustic emission signals, including in temporal and frequency domains, and/or the imaging signals).

Provided is an embedding analysis-based facility predictive maintenance method performed by a server, including (a) collecting time-series operation data of at least one machine; (b) deriving abnormal state information to determine whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information; (c) building an abnormal pattern analysis model by performing machine learning on each mapped information and analyzing whether the embedding result pattern indicates abnormality or normality; and (d) when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis model to derive current state information and future prediction state information.

A method for monitoring the machine geometry of at least one gear cutting machine (10), having the following steps: a) measuring a workpiece in a measuring device (20) in order to determine actual data, wherein a workpiece is concerned which was previously machined in the machine (10) on the basis of specification data (VD, ΔVD, MD, ΔMD); b) correlating the actual data with the specification data (VD, ΔVD, MD, ΔMD) in order to thus determine the deviation of a geometric setting of at least one axis of the machine (10); c) storing the deviation of the geometric setting; d) repeating the steps a)-c) after the machining of further workpieces in the machine (10); e) performing a statistical evaluation of several of the stored deviations in order to determine a geometric change in the axis of the machine (10) by considering a predetermined condition and/or rule.

A hot water supply device including an inlet pipe, an outlet pipe, a burner unit, a heat exchanger, an exhaust aperture, a first temperature sensor detecting a measured exhaust temperature of the exhaust gas, a second temperature sensor detecting a water temperature of water entering the inlet pipe, and a processor. The processor is configured to obtain an error between the measured exhaust temperature and an estimated exhaust temperature, and detects that scale clogging has occurred inside the heat exchange tubing based on an index which is generated using the error between the measured exhaust temperature and the estimated exhaust temperature. The estimated exhaust temperature is a first predetermined value that is determined using a numerical equation which has at least the water temperature of water entering the inlet pipe and a scale number of the hot water supply device as variables of the numerical equation.

There are provided a slave, a work machine, and a method for storing log information, which are capable of appropriately store the log information when a communication abnormality occurs such that communication cannot be kept between a master and the slave in an industrial network. The control section performs first storage processing storing the log information into a volatile storage section when a communication abnormality occurs such that communication with the master cannot be kept, communication abnormality determination processing for determining whether the communication abnormality has occurred, and second storage processing storing the log information into a non-volatile storage section by acquiring the log information from the volatile storage section, in response to a determination made in that the communication abnormality has occurred as a result of the communication abnormality determination processing.