Provided is a method and a device for detecting open and closed states of a platen cover plate of an image reading apparatus. The method includes: controlling a scanning element to scan light at an edge of a platen to obtain a signal value representing a light intensity; determining whether an absolute difference value between the signal value and a preset standard value is greater than a preset tolerance value; sending a prompt signal to prompt a user to close the platen cover plate in response to the absolute difference value being greater than the preset tolerance value; and continuing to perform scanning work on a manuscript on the platen in response to the absolute difference value being not greater than the preset tolerance value. The influence on quality of the scanned image due to incomplete closing of the platen cover plate is prevented.

An asset protection, monitoring, or control device is operative to execute a decision-making logic to process inputs and generate a decision-making logic output that comprises one or more time series, process the decision-making logic output using a machine learning model, and cause an action to be performed responsive to a machine learning model output.

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.

A plant operation data monitoring device comprises: an input section that receives operation data on a plant; and a calculator that includes databases storing the operation data received, and a computing section executing a program. The computing section stores the operation data received in a first database of the databases in time series. The computing section determines from peak values of the operation data stored whether gradients of the operation data are positive or negative, and then stores the gradients in a second database of the databases for positive gradients or in the second database of the databases for negative gradients in time series. The computing section determines threshold values for abnormality determination about the positive and negative gradients, divides the positive gradients and the negative gradients into normal values and abnormal values, and additionally stores the divided gradients in the second database for the positive or negative gradients.

The present invention relates to a condition monitoring device and method for monitoring an electrical machine. The method includes obtaining, at periodic instants, measurements from sensors of the condition monitoring device, where each sensor is one of a magnetometer and an accelerometer. The method also includes comparing, for one or more instants, amplitude data of the measurements with condition monitoring data, wherein the comparison is performed for the amplitude data in one or more axes and at one or more frequencies. The condition monitoring data includes a relation between a plurality of parameters, a plurality of conditions and a plurality of frequencies. The method additionally includes detecting a condition and at least one parameter associated with the condition, based on the comparison. According to the detection, the method includes utilizing the measurements of the at least one parameter for determining a health condition of the electrical machine.

Methods and systems for operating an aircraft engine. A health parameter for the aircraft engine is monitored by a health evaluation device, the health parameter received from a first instrument. the health parameter is compared, by the health evaluation device, to a predetermined threshold. When the health parameter reaches the predetermined threshold, the health evaluation device wirelessly transmits a fault signal to a controller associated with the aircraft engine to elicit a health response from the controller, the fault signal containing at least two mutually-exclusive fault codes associated with an operating condition of the aircraft engine monitored by a second instrument.

The present invention provides test data for various machines for testing an application. This data management device comprises: an input unit that accepts input of data output from at least one device; a storage unit that stores, as test data, the test data input via the input unit; a communication control unit that acquires, from the storage unit, test data requested on the basis of a request from an external device, and controls transmission of the requested test data to the external device; and a communication unit that transmits the requested test data to the external device on the basis of the transmission control by the communication control unit.

The present invention discloses an edge-cloud collaborative fault detection method for a low-voltage distribution network based on a random matrix theory. An edge-cloud collaborative way is adopted, including fast fault detection running in an edge IoT agent and fault timing and locating analysis running in a distribution network control center. At an edge IoT terminal, the fault is quickly detected based on time-delay correlation analysis, a long-time series model is constructed, a time series is fitted with an autoregressive moving average model, and the fault is quickly judged based on a typical value of a limit spectral density function of the time series; after the edge IoT terminal detects the fault, fault-related data are uploaded to the distribution network control center through data screening, historical data and real-time data are integrated, and a spectral deviation index is configured to perform fault timing and locating analysis.

Provided are a refrigerator and a cloud server that diagnose a cause of an abnormal state. According to an embodiment of the present disclosure, an abnormal state diagnosis unit included in the refrigerator or the cloud server generates information on the abnormal state of the refrigerator based on similarity between stored first group of information and a normal pattern, and a cause diagnosis unit generates, when the abnormal state diagnosis determines that a state of the refrigerator is an abnormal stte, information on cause of the abnormal state based on similarity between stored second group of information and a defect pattern.

A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.