Disclosed is an intelligent remote monitoring method for fire-fighting, including: S1, pre-loading a protocol parsing configuration file in JSON format; S2, acquiring fire-fighting status information and operation status information of fire-fighting IoT equipment to obtain a data frame of a binary protocol; S3, parsing the data frame according to the configuration file to obtain a JSON data object, and pushing the parsed data of the fire-fighting IoT equipment to a routing layer through data push technology including kafka technology and storing in a fire-fighting database; S4: presetting different application modules according to different application services, and determining a routing direction of the data of the fire-fighting IoT equipment according to rules including a device type and a device status, and routing the data of the corresponding fire-fighting IoT equipment to the corresponding application module through the routing layer for triggering processing of events including alarms and faults, so as to realize remote monitoring of fire-fighting.

Detecting a breakover event during a friction test includes obtaining a hookload measurement for each of a series of time windows and generating a linear model and a nonlinear model from the plurality of hookload measurements. During run time, from the nonlinear model, an inflection point is identified from the nonlinear model, where the inflection point is determined to have occurred at a particular time window. A hookload value associated with the linear model, and a hookload value associated with the nonlinear model is determined for the particular time window. A breakover event is determined to have occurred at the particular time when the hookload value associated with the linear model at the particular time window exceeds the hookload value associated with the nonlinear model at the particular time window.

Systems and methods may provide for determining an amount of physical bend in an electronic device and comparing the amount of physical bend to a threshold. Additionally, a warning may be generated if the amount of physical bend exceeds the threshold. In one example, one or more values representing the amount of physical bend are stored to a nonvolatile memory on the device and retrieved in accordance with one or more of a diagnostic push event or a diagnostic pull event.

A device may generate one or more objects for one or more transactions. The one or more transactions may include a sequence of one or more events. The one or more objects may include one or more states associated with the sequence of one or more events. The device may receive information associated with the one or more transactions. The information may be used to update the one or more states of the one or more objects. The device may detect one or more alerts based on the one of more objects. The one or more alerts may be associated with the one or more states of the one or more objects. The device may determine an alarm status based on the one or more alerts and may provide information to cause an action to be performed.

A method and system for seal failure annunciation comprises a process connector connected to a probe used to measure a product level in a tank. A pulse generation module generates a pulse that is propagated through a voided space in the process connector and a detector module configured to receive the echo curve from the interrogation pulse. A logic module is used to evaluate the received echo curve to determine if a seal in the process connector has failed. When the logic module indicates seal failure, an alarm module initiates an alarm indicating said seal in said process connector has failed.

A system for anticipating low-speed bearing failure triggers a notification when a noise generated by the low-speed bearing exceeds a threshold. The system predicts failure far in advance of the actual failure. The system includes an accelerometer for detecting the noise generated by the bearing. The signal produced by the accelerometer is processed using a band pass filter, an amplifier/rectifier, an averaging filter, and a voltage to current converter. The signal and raw data are transmitted to a remote monitoring system, such as a computer. The signal is further analyzed, such as to produce a best-fit line. When the signal exceeds a predetermined threshold, such as when the amount or the slope of the best-fit line exceeds a value, the remote system notifies a monitor to schedule maintenance.

Provided are an acoustic sensor and a home appliance system comprising the same. The acoustic sensor comprises a communication unit, a microphone to collect an acoustic signal, a memory to store a failure acoustic signal of a home appliance, and a processor, wherein in response to the acoustic signal, collected by the microphone, corresponding to the failure acoustic signal of the home appliance, the processor transmits the collected acoustic signal, or data corresponding to the collected acoustic signal, to an external server or a terminal. Accordingly, failure of the home appliance may be easily diagnosed.

In an abnormal work detecting method for an industrial device, running data of an industrial device is obtained at a predefined time interval. The running data is determined whether the running data falls with a range of a predefined initial data, the range of the predefined initial data indicating the industrial device is normally running. If no, a position of the detection device is obtained by the position device. A warning message is generated to remind a maintainer of the industrial device a position where the industrial device works abnormally.

The present disclosure is directed to a system for determining sensor condition. A sensor signal generated by a sensor to communicate the current condition of an aspect being monitored by the sensor may also be employed to determine the condition of the sensor itself. For example, a device capable of determining if the sensor condition is normal or malfunctioning (e.g., erratic, stuck, etc.) may comprise a monitoring module (MM) to receive the sensor signal. The MM may comprise a sensor condition determination module (SCDM) to determine sensor condition. The SCDM may include a feature extraction engine to determine various characteristics of (e.g., to “extract features” from) the sensor signal and a model to determine sensor condition based on the extracted features. The model may include a support vector machine (SVM) taught to determine sensor condition utilizing sampled sensor signals correlated with annotations of sensor condition.

The present invention provides a production equipment monitoring method and system, and the method comprises: receiving production equipment alarm information; determining abnormal production equipment according to the production equipment alarm information, and determining a user group corresponding to the abnormal production equipment according to preset correspondence between production equipment and user group; and sending the production equipment alarm information to a user in the determined user group in a preset alarm manner. As a result, alarm information can be sent to the corresponding user(s) in the preset alarm manner the first time an abnormity in the production equipment is found, so as to notify related personnel to handle the abnormity timely and accurately.