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3079 results about "Condition monitoring" patented technology

Condition monitoring (or, colloquially, CM) is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.), in order to identify a significant change which is indicative of a developing fault. It is a major component of predictive maintenance. The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences. Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure. Condition monitoring techniques are normally used on rotating equipment, auxiliary systems and other machinery (compressors, pumps, electric motors, internal combustion engines, presses), while periodic inspection using non-destructive testing (NDT) techniques and fit for service (FFS) evaluation are used for static plant equipment such as steam boilers, piping and heat exchangers.

Reversible electrochemical mirror (REM) state monitoring

Reversible electrochemical mirror (REM) devices typically comprise a conductive oxide mirror electrode that is substantially transparent to radiation of some wavelengths, a counter electrode that may also be substantially transparent, and an electrolyte that contains ions of an electrodepositable metal. A voltage applied between the two electrodes causes electrodeposition of a mirror deposit on the mirror electrode and dissolution of the mirror deposit on the counter electrode, and these processes are reversed when the polarity of the applied voltage is changed. Such REM devices provide precise control over the reflection and transmission of radiation and can be used for a variety of applications, including smart windows and automatically adjusting automotive mirrors. According to the present invention, measurements of the sheet resistance of the mirror electrode in a REM device are correlated with the thickness of electrodeposited mirror metal and can be used to monitor the reflectance of the device. Sheet resistance measurements can be performed while the mirror state of the device is being switched if adequate isolation between the measurement and switching circuits is provided. This can be accomplished by use of external resistors or more sophisticated circuitry, or by taking advantage of the relatively high sheet resistance of the mirror electrode itself. Monitoring the reflectance of REM devices according to this invention provides significant cost and performance advantages.

GIS device state intelligent monitoring system and method based on edge computing technology

The invention relates to a GIS device state intelligent monitoring system and method based on the edge computing technology. The system includes a state sensor terminal, a data node device and an intelligent monitoring center, wherein the state sensor terminal is used for gathering collected state data to the data node device to perform power IoT edge calculation and is connected with the intelligent monitoring center through the transmission network, the condition monitoring sensor terminal is used for acquiring related state parameters in real time and receiving and performing limited edge calculation tasks, the data node device is used for collecting the data and receiving and performing the edge calculation tasks, and the intelligent monitoring center is used for performing equipment IoT management, intelligent evaluation analysis and advanced application for a GIS device state and configuring an optimized edge calculation algorithm model and an SG-CIM data model to the data node device. The system is advantaged in that a circuit breaker GIS equipment Internet of Things system of a substation is constructed, and the automation, informationization and intelligence level of equipment state evaluation and diagnosis can be improved.

Device for monitoring state of power transmission line tower-line system

The invention discloses a device for monitoring the state of a power transmission line tower-line system, which fully utilizes the advantages of corrosion resistance, wide operation temperature range, anti-electromagnetic interference, passivity, long measuring distance, reliable operation and long service life and the like of optical fiber sensors to configure the optical fiber sensors on a transmission conductor and a tower in a quasi-distributed structure for detecting the temperature, the strain and the acceleration of the transmission conductor and the multipoint stress variation on the tower which are transmitted to an optical modulator demodulator for demodulating and are sent to a computer analyzing and processing system for calculating to obtain the icing quality, the waving amplitude, the breeze vibration amplitude and frequency and the sag of the transmission conductor, and the inclination angle of the tower, thus realizing the simultaneous monitoring on the icing, the waving, the breeze vibration and the sag of the transmission conductor, and the inclination angle state of the tower, and being capable of finishing long-time reliable operation under the on-site severe environment conditions.

System and method thereof for evaluating real-time running state of wind generating set

The invention relates to the technical field of running state monitoring of wind generating sets, in particular to a system and a method for evaluating the running state of a wind generating set. The evaluating system mainly comprises a monitoring module, a quantifying module, a weight confirmation module and an evaluating module; data monitored in real time by a wind generating set control system are utilized as the input of the evaluating system of the real-time running state; then the data are quantified to obtain a real time deterioration degree of each evaluating index; finally, when the deterioration degree of a single item evaluating index and a permissible value have greater deviation, the serious evaluating result is directly given, otherwise, the weight module and the evaluating module are used for calculating an evaluating result of the running state of the generating set, and the evaluating result is used as the output of the system. The invention provides scientific basis for state maintenance of the wind generating set, and provides technological supports for ensuring high-efficient, reliable and safe running of the wind generating set, evaluating and forecasting the operational reliability of an electric power system of a windy electric field, and has important engineering application value.

Adaptive extraction and diagnosis method for degree features of mechanical fault through stack-type sparse automatic coding depth neural network

The invention relates to an adaptive extraction and diagnosis method for degree features of a mechanical fault through a stack-type sparse automatic coding depth neural network, and belongs to the technical field of mechanical equipment state monitoring and reliability evaluation. The method aims at a problem of intelligent diagnosis of the degree of the mechanical fault, and comprises the steps: carrying out the stacking of sparse automatic coding, adding a classification layer, and constructing the stack-type sparse automatic coding depth neural network which integrates the adaptive learning and extraction of the degree features of the fault and fault recognition; employing the advantage that the sparse automatic coding can automatically learn the internal features of data, and adding noise coding to be integrated in the sparse automatic coding for improving the robustness of feature learning; carrying out the layer-by-layer no-supervision adaptive learning and supervision fine tuning of the original input complex data through multilayer sparse automatic coding, completing the automatic extraction and expression of the degree features of the mechanical fault and achieving the intelligent diagnosis of the degree of the fault. The method is used for the diagnosis of the degree of faults of rolling bearings under different work conditions, and obtains a good effect of feature extraction and diagnosis.
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