A remote water heater monitoring system is configured to communicate with a plurality of client water heaters over a network. The system processes received data related to the operation of the plurality of client water heaters and identifies one or more baseline trends over time related to water heater performance and/or water heater reliability using the received data from one or more of the client water heaters and identifies one or more abnormalities in the operation of a particular one of the client water heaters based on the baseline trends. An alert is generated for one or more of the abnormalities in the operation of the particular one of the client water heaters and is associated with a corresponding client account.

The present disclosure provides a method and a system for determining a maintenance time of a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of the system and gas leakage information of the pipe network; extracting feature information based on the running time and the gas leakage information; generating a pipe network maintenance value through a maintenance value prediction model based on pipe network maintenance information and pipe network environment information, the pipe network maintenance value reflecting a priority of pipe network maintenance processing; and predicting the maintenance time of the pipe network based on the feature information and the pipe network maintenance value using a maintenance time prediction model, the maintenance time prediction model being a machine learning model.

Herein provided are methods and systems for detecting failure of a sensor in a control system for a gas turbine engine. A signal is received from the sensor. A high-pass filter is applied to the signal to produce a high-frequency component signal. A rate of occurrence of signal spikes in the high-frequency component signal is determined. The high-frequency component signal is compared to a component signal threshold which is based on at least one known healthy component signal and at least one faulty component signal. The presence of intermittent open circuits caused by the sensor is detected based on the comparing and on the rate of occurrence of signal spikes.

To automate alarm management in modular production installations, provided is, with respect to a modular production installation for which, for its modular, manufacturerindependent construction and operation, process equipment assembly modules, controlled by a programmable PEA controller and simulated by means of a configurable PEA simulation, are integrated into the production installation, by means of a module type package <MTP> mechanism for the PEA module description, in the course of a process orchestration which is standardized the following is carried out in the alarm management: all states of the modular production installation with respect to triggered faults of possible fault cases of the production installation are virtually run through and simulated; simulation faults that occur are logged concomitantly by means of alarms, which provide identification and an alarm message log is created and/or alarm chains are identified and alarm relationships in the alarm chains are determined.

Herein provided are methods and systems for detecting failure of a sensor in a control system for a gas turbine engine. A signal is received from the sensor. A high-pass filter is applied to the signal to produce a high-frequency component signal. A rate of occurrence of signal spikes in the high-frequency component signal is determined. The high-frequency component signal is compared to a component signal threshold which is based on at least one known healthy component signal and at least one faulty component signal. The presence of intermittent open circuits caused by the sensor is detected based on the comparing and on the rate of occurrence of signal spikes.

The present disclosure includes a system diagnosis method in an energy management system for electrical energy and at least one additional form of energy. The method includes acquiring actual values of one or more operating parameters; comparing the actual values with target values of the operating parameters in order to obtain a deviation; determining whether the deviation of the actual values of the operating parameters from the target values of the operating parameters exceeds a deviation threshold value; determining existence of a malfunction and where it is occurring when the deviation exceeds the deviation threshold value; assigning the malfunction to predefined malfunction groups based on the deviation; and producing a notification signal.

To automate alarm management in modular production installations, provided is, with respect to a modular production installation, for which, for its modular, manufacturer-independent construction and operation, process equipment assembly modules, controlled by a programmable PEA controller and simulated by means of a configurable PEA simulation, are integrated into the production installation, by means of a module type package <MTP> mechanism for the PEA module description, in the course of a process orchestration which is standardized the following is carried out in the alarm management: all states of the modular production installation with respect to triggered faults of possible fault cases of the production installation are virtually run through and simulated; simulation faults that occur are logged concomitantly by means of alarms, which provide identification and an alarm message log is created and/or alarm chains are identified and alarm relationships in the alarm chains are determined.

Herein provided are methods and systems for detecting failure of a sensor in a control system for a gas turbine engine. A filtered signal is received from the sensor. A high-pass filter is applied to the filtered signal to produce a high-frequency component signal. A rate of occurrence of signal spikes in the high-frequency component signal is determined. The high-frequency component signal is compared to a component signal threshold based on at least one known healthy component signal and at least one faulty component signal, wherein the faulty component signal is based on a known faulty signal caused by intermittent open circuits. The presence of intermittent open circuits caused by the sensor is detected based on the comparing and on the rate of occurrence of signal spikes.

As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

A signal processing method includes: acquiring first measurement data based on a signal output from a first sensor configured to detect a physical quantity of a first axis generated by a vibration of an object and second measurement data based on a signal output from a second sensor configured to detect a physical quantity of a second axis generated by the vibration of the object; generating a Lissajous figure based on the first measurement data and the second measurement data; transforming coordinates of each point in the Lissajous figure into polar coordinates and generating time series data of a first angle which is an angle formed between the first axis and a straight line, the straight line being obtained by projecting a straight line passing through an origin and each point in the Lissajous figure onto a plane including the first axis and the second axis; and executing frequency analysis on the time series data of the first angle and calculating a first maximum peak intensity which is a maximum peak intensity in a first frequency spectrum obtained by the frequency analysis.