A system for automatic application of fault detection and diagnostics (FDD) rules within a building management system (BMS) includes a memory having instructions stored thereon and a processor configured to execute the instructions. The processor, in executing the instructions, may retrieve from a plurality of BMS devices, a first BMS device having at least one attribute. The processor may identify, from a plurality of FDD rules based on the at least one attribute, an FDD rule associated with the at least one attribute. The processor may apply the FDD rule to the data point of the first BMS device corresponding to the at least one attribute. The processor may, responsive to determining a second BMS device from the plurality of BMS devices shares the at least one attribute, automatically apply the FDD rule to the second BMS device.

An apparatus is described herein related to an environmental monitoring system configured to avoid spoilage of goods by alerting a supervisor to hazardous conditions. The sensors monitor a plurality of parameters, of which relative humidity is of particular importance. The system offers multiple alternative ways of detecting airing events including time series anomaly discovery. The system also offers different ways of processing data related to airing events and of allowing a user to monitor either the presence or the absence of airing events.

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

The present invention relates to a precise predictive maintenance method for a driving unit and the configuration thereof includes a first base information collecting step S10 of collecting change information of an energy size in accordance with a time measured in a normal driving state of the driving unit; a second base information collecting step S20 of collecting a peak interval; a setting step S30 of setting an alarm upper limit and an alarm lower limit for the peak interval; and a detecting step S40 of measuring a peak interval.

A medical device is configured to monitor, using one or more sensors, an environmental condition of a physical environment proximate to a medical device, and to determine when a value representative of the environmental condition exceeds a threshold for safe operation of the medical device with regard to care of a patient. Responsive to the value exceeding the threshold, an operation mode of the medical device is automatically switched from a first mode currently programmed for the care of the patient to a second mode, the second mode using different parameters than the first mode to control the medical device.

A medical device is configured to monitor, using one or more sensors, an environmental condition of a physical environment proximate to a medical device, and to determine when a value representative of the environmental condition exceeds a threshold for safe operation of the medical device with regard to care of a patient. Responsive to the value exceeding the threshold, an operation mode of the medical device is automatically switched from a first mode currently programmed for the care of the patient to a second mode, the second mode using different parameters than the first mode to control the medical device.

According to an embodiment, an accounting apparatus includes a change machine, a detection device, and a controller. The controller determines whether or not the change machine is capable of paying change in cash on the basis of a result of a detection by the detection device. The controller accepts, when determining that the change machine fails to pay the change in cash, a payment method selected by a customer from a plurality of change payment methods other than paying in cash.

An arc fault detection method for a photovoltaic system based on an adaptive kernel function and instantaneous frequency estimation includes steps of: sampling signal x.sub.t in a time window length of T.sub.NCT and obtaining an iterative time-frequency diagram of x.sub.t by nonlinear chirplet transform; extracting detection variables based on frequency component in the selected iterative time-frequency diagram to determine a moment when spectrum energy increases; when the moment is found, obtaining a matrix distribution form of the x.sub.t in a time-frequency domain obtained by the adaptive optimum kernel time-frequency representation, and processing the matrix with sum of squares in a time dimension to obtain a column vector; processing each selected frequency bands with integration operation in a frequency dimension to obtain multiple detection variable values as inputs of a well-trained Naive Bayes model; and determining photovoltaic system states in a current period.

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.

A plant maintenance assisting system is provided with: an abnormality indication monitoring system; an abnormality diagnosis system; a maintenance plan creation system; a risk evaluation system; and a component management system. On the basis of risk information and abnormality diagnosis information that is a result of diagnosis by the abnormality diagnosis system, the risk evaluation system determines the need for maintenance work with respect to an abnormality indicating facility. In a case where it is determined that maintenance work with respect to the abnormality indicating facility is necessary, the maintenance plan creation system sets, in a maintenance plan, maintenance implementation timing of maintenance work with respect to a facility including the abnormality indicating facility. On the basis of component management information, the component management system determines whether a component can be procured at the maintenance implementation timing set by the maintenance plan creation system.