A method and arrangement keep track of the cumulative absolute drift of a measurement device, in connection with calibration actions performed by a calibrator. Measurement results are saved (22) in each measurement instant, and if they exceed a threshold value (23), an adjustment step is performed (24). Irrespective of the adjustment steps, the latest drift value (25) during the latest calibration interval is summed (26) with the previous cumulative drift value. The cumulative drift value, i.e. AbsDrift, is saved to a server, from where it can be illustrated visually (27). If there is an uncommon pattern or large cumulative drift present, an indication or alarm (28) can be sent to the user of the calibrator.

A system for monitoring an electrical device and a method thereof is provided, wherein at least one primary unit is connected with an input side and/or an output side of the electrical device and configured to obtain electrical parameters of the electrical device. A secondary unit is connected with each primary unit and configured to receive the electrical parameters obtained by the primary unit, store the electrical parameters to form a repository of previously obtained electrical parameters, compare the electrical parameters with previously obtained electrical parameters to determine any deviation in the electrical parameters and correlate the deviation in electrical parameters with a list of impending faults to identify any impending fault. The secondary unit based upon the correlation of the electrical parameters generates a summary and/or alert indicative of state of the electrical device by way of alerts, notifications etc.

An example method of performing maintenance on an aircraft includes receiving a vehicle data-signature-map of an interior of an aircraft for at least one parameter of the aircraft, and the vehicle data-signature-map is based on sensor outputs received from sensors of mobile devices positioned at locations in the interior of the aircraft. The method also includes comparing the vehicle data-signature-map with a previous vehicle data-signature-map, based on differences of the vehicle data-signature-map as compared to the previous vehicle data-signature-map making a determination for maintenance, and generating and outputting a recommendation for inspection of an identified portion of the aircraft based on the distribution of the at least one parameter in the vehicle data-signature-map for the identified portion of the aircraft being substantially different than the distribution of the at least one parameter in the vehicle data-signature-map for other portions of the aircraft to assist with the maintenance of the aircraft.

ABSTRACT A control method of a measurement supplied by a first turbomachine sensor by a first channel, and by a second turbomachine sensor by a second communication channel includes: acquiring the first signal from the first communication channel and the second signal from the second communication channel; determining of a validity status of each of the acquired signals; and transmitting of a signal to be processed.

An example system for determining a performance status of an environmental control system (ECS) in a vehicle includes memory and processing circuitry. The processing circuitry is configured to determine, based on one or more of the aircraft data, the weather data, or the trending data, an estimated compressor exit temperature. The processing circuitry is configured to determine a current residual compressor exit temperature based on the estimated compressor exit temperature and a current compressor exit temperature. The processing circuitry is configured to determine whether a residual condition is met based at least in part on the residual compressor exit temperature and whether a pack condition is met based at least in part on a pack temperature. The processing circuitry is configured to provide an indication of the performance status of the ECS based on at least one of the residual condition or the pack condition being met.

A method for health assessment of a system including a transport apparatus including registering predetermined operating data embodying at least one dynamic performance variable output by the transport apparatus, determining a base value (C.sub.pkBase) characterized by a probability density function of each of the dynamic performance variable output, resolving from the transport apparatus in situ process motion commands of the apparatus controller and defining another predetermined motion set of the transport apparatus, registering predetermined operating data embodying the at least one dynamic performance variable output by the transport apparatus and determining with the processor another value (C.sub.pkOther) characterized by the probability density function of each of the dynamic performance variable output by the transport apparatus, and comparing the other value and the base value (C.sub.pkBase) for each of the dynamic performance variable output by the transport apparatus respectively corresponding to the predetermined motion base set and the other predetermined motion set.

A system and method for recognizing and forecasting anomalous sensory behavioral patterns of a machine, including: monitoring a first set of time-stamped sensory input data related to at least one machine; determining, upon analysis of the first set of time-stamped sensory input data, a first suspicious pattern of a first anomalous sensory input behavior associated with the first set of time-stamped sensory input data; comparing the first suspicious pattern to a second pattern of a second anomalous sensory input behavior that is associated with a second set of time-stamped sensory input data, wherein the second pattern previously determined to be indicative of a machine failure; and, determining if the first suspicious pattern is correlated above a predetermined threshold with the second pattern.

The present disclosure relates to an apparatus for diagnosing a state of a photovoltaic device, a building Integrated Photovoltaics (BIPV) device, etc., and more particularly to an apparatus for diagnosing photovoltaic power generation, which diagnoses a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc., wherein the apparatus processes power generation information, which is collected from the photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and performs precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located, so that a power generation trend can be analyzed with improved accuracy of analysis of state.

A computer implemented method includes turning off a sensor, receiving fall curve data from the sensor, and comparing the received fall curve data to a set of fall curve signatures to identify the sensor or a sensor fault.

Methods for monitoring a machine are described. In one aspect, a method includes receiving information on a plurality of events associated with the machine, determining a severity value for at least one event of the plurality of events, the severity value based on at least one of a safety value, a hierarchy value, a time-to-repair value, and a cost-of-repair value, and outputting an alert includes the severity value if the severity value exceeds a predetermined threshold associated with the at least one event. Systems and machine-readable media are also described.