549 results about "Signal variation" patented technology

The invention relates to the technical field of communication engineering, and discloses a power line carrier and wireless dual-mode fusion optimized communication method, which comprises the following steps of: 1, acquiring signal quality, noise and delay data of a power line and a wireless link in real time, and constructing a prediction model by utilizing historical data; step 2, dynamically adjusting a judgment threshold value of link switching in real time based on the prediction model, and ensuring that switching is triggered only when the signal change exceeds a preset range; step 3, based on the dynamic threshold, combining multiple indexes of signal-to-noise ratio, delay, packet loss rate and network load; according to the method, the necessity of link switching can be more intelligently judged by dynamically adjusting the judgment threshold of link switching and adopting a fuzzy logic decision mechanism, frequent switching during tiny fluctuation of signals is avoided by dynamic threshold adjustment, and a fuzzy logic system integrates multiple factors, so that the decision is finer, and the reliability of link switching is improved. And unnecessary delay and resource waste are reduced, so that the stability of a communication system is improved.

The invention relates to the technical field of power system monitoring and control, and discloses a power grid oscillation monitoring system and method based on an intelligent sensor, and the system comprises an intelligent sensor module which is used for collecting the signal data of the operation of a power grid, is internally provided with a self-adaptive sampling mechanism, dynamically adjusts the sampling frequency to achieve the capturing of the signal change, and meanwhile, transmits the signal data to the intelligent sensor module; de-noising and preprocessing are carried out on the collected signal data, the processed signal data are transmitted to an edge calculation module, and high-quality input is provided for oscillation signal feature extraction; and the edge calculation module is used for receiving the preprocessed signal data from the intelligent sensor module. Through the intelligent sensor module, a self-adaptive sampling mechanism is combined with multi-mode signal acquisition, the rapid change characteristic of sampling frequency capture signals is dynamically adjusted, accurate capture of oscillation signals is ensured, the redundant calculation burden and energy consumption of the system can be reduced while the precision is ensured, and technical innovation is provided for real-time monitoring of a complex oscillation scene of a power grid.

The invention relates to the technical field of power transmission line anomaly recognition, and particularly discloses a power transmission line anomaly recognition model construction method combined with edge calculation, which comprises the following steps of: gradually increasing electromagnetic interference intensity test, acquiring current and voltage signals in real time, and monitoring electric field intensity and signal change by combining a high-precision sensor; dynamically calculating an influence value of electromagnetic interference; recursively estimating the dynamic influence of the electromagnetic interference through a Kalman filter, accurately modeling the influence of the electromagnetic interference by combining the system state and measurement data updating, and realizing the real-time evaluation of the influence of the interference on the system by calculating an electromagnetic interference coefficient and an electromagnetic shielding efficiency expected deviation; according to the method, a spectral analysis and high-frequency current and voltage fluctuation feature extraction method is adopted, and accidental interference and system faults are effectively distinguished by calculating a spectral ratio; through multi-level data analysis and optimization, real fault signals and electromagnetic interference signals can be accurately distinguished, and false alarms and missing alarms are reduced.

The invention relates to the technical field of industrial equipment management, in particular to an intelligent power equipment inspection system based on artificial intelligence, which comprises a fault mode generation module, a fault detection module, a fault classification module, an inspection feature optimization module and an anomaly prediction and evaluation module. According to the method, the comprehensiveness of equipment state sensing is optimized by collecting the multi-dimensional operation parameters of the power equipment and analyzing the signal amplitude distribution and frequency domain change, the potential fault features are accurately recognized by combining the dynamic feature range and the signal distribution deviation matching, the signal anomaly detection accuracy is improved, and the fault detection accuracy is improved. The method comprises the following steps: extracting differentiated characteristic values of current and temperature signals, optimizing fault type classification, improving the positioning capability of a problem root, dynamically extracting and matching a signal trend, generating an inspection parameter matrix, realizing global dynamic monitoring of an operation state, and predicting a model based on a signal change rate and a time node range. The real-time performance and precision of abnormal trend prediction are improved, and the reliability of equipment operation and maintenance is enhanced.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention provides a non-intrusive diagnosis method for typical rotor faults of a synchronous phase modifier, and the method comprises the steps: carrying out finite element modeling and fault working condition setting, carrying out simulation to obtain magnetic field distribution data under each working condition, carrying out the extraction and feature analysis of stray magnetic field signals, extracting time domain and frequency domain signals of a stray magnetic field under healthy and various fault working conditions, and carrying out the diagnosis of the typical rotor faults of the synchronous phase modifier. Fundamental wave, odd-even harmonic components and signal change trends are compared and analyzed, stray magnetic field characteristics corresponding to faults are extracted and recognized, diagnosis criterion establishment and fault recognition are carried out, diagnosis criteria and threshold values of rotor turn-to-turn short circuit, dynamic eccentricity and shaft-diameter composite eccentricity faults are established according to the stray magnetic field characteristics, and the fault diagnosis accuracy is improved. And when the specific harmonic amplitude or change characteristic of the stray magnetic field signal exceeds the criterion threshold, determining the rotor fault of the corresponding type and degree. Equipment dismounting or structure reconstruction is not needed, the detection system is simple in structure, internal fault characteristics of the phase modifier can be reflected in real time, and high sensitivity and practicability are achieved.

The invention discloses a transformer iron core vibration monitoring and fault diagnosis method based on voiceprint characteristics, and belongs to the technical field of power equipment monitoring and fault diagnosis, and the method comprises the steps: obtaining voiceprint signals of each monitoring point about the vibration of an iron core in a preset transformer, and collecting the voiceprint characteristics according to the voiceprint signals of different monitoring points, analyzing the change rule of each voiceprint signal; according to the change rule, distinguishing the change of the voiceprint signal according to light change and moderate change, and obtaining the operation state of the preset transformer under the condition of light change; the operation state comprises current parameters of a preset transformer; and giving a monitoring period according to the light change, and presetting a critical threshold value related to the vibration frequency change of the voiceprint signal in different monitoring periods. According to the invention, by monitoring and analyzing the voiceprint signal and combining the technical means of multiple monitoring points, dynamic monitoring and enhanced learning, early fault early warning and accurate diagnosis of the operation state of the transformer are realized, and the method has relatively high practicability and innovativeness.

The invention provides an LED lamp adaptive speed regulation heat dissipation method and system based on real-time junction temperature monitoring. The method comprises the following steps: S100, acquiring a PWM signal or an analog signal of an LED driving command signal in real time; s200, on the basis of the change of the driving instruction signal, feedforward prediction is carried out by using a dynamic thermal inertia mapping model, and a feedforward fan control quantity is generated; s300, LED junction temperature is monitored in real time through a forward voltage method, and feedback fan control quantity is generated through a feedback controller according to the deviation between the real-time junction temperature and the target junction temperature; and S400, synthesizing the feedforward control quantity and the feedback control quantity to generate a fan PWM control signal, outputting the fan PWM control signal to a fan driving circuit, and adjusting the rotating speed of the cooling fan. According to the method, driving signals and junction temperature information are processed in parallel, and feedforward and feedback control is combined, so that self-adaptive accurate regulation and control are realized, thermal load change is quickly responded, system lag is effectively inhibited, and LED heat dissipation performance is optimized.

The invention discloses a calculation method and system for multi-point detection of a gas nonmetal pipeline based on an active sound source, and relates to the technical field of pipeline monitoring and management, and the method comprises the following steps: 1, monitoring the change of a sound signal in the gas nonmetal pipeline according to the change of a transmitted laser signal through an optical fiber of a distributed acoustic assembly as a sensing medium, according to the invention, the optical fiber of the distributed acoustic assembly is used for monitoring the change of the acoustic signal in the gas non-metal pipeline, the frequency offset, intensity and delay change of the laser signal are converted into the frequency, amplitude intensity and sound wave source of the acoustic signal in a correlation manner, and the adaptive filter is used for dynamically updating the filter coefficient. A window function is adopted to carry out framing processing on sound signals, short-time Fourier transform is applied, real classification labels are distributed to the sound signals through a random forest algorithm, a triangulation positioning method is applied to determine space coordinates of the sound signals, and accurate monitoring and classification of the pipeline state are achieved.

The invention discloses a distribution box operation state monitoring method and system, and relates to the field of distribution boxes, and the system comprises an acquisition module which is used for collecting current and voltage signals of each loop in a distribution box, surface temperature distribution of a box body and opening and closing state signals of a cabinet door, and converting the collected analog signals into a digital signal sequence; the extraction module is used for receiving the digital signal sequence and extracting a signal fluctuation feature, a temperature field distribution feature and a state signal change feature to generate a three-dimensional feature set; the system can obtain loop electrical parameters, box body temperature and cabinet door state in real time, accurately analyze operation characteristics to deduce state trend, quickly position defective loop components and influence range in case of abnormity, scientifically evaluate health level, timely give an early warning, synchronously disconnect power supply of associated equipment in case of severity, effectively reduce fault shutdown, and improve work efficiency. The power utilization safety and the maintenance efficiency are improved, and stable operation of the distribution box is guaranteed.

The present invention relates to the technical field of industrial equipment management, specifically an intelligent power equipment inspection system based on artificial intelligence. The system includes: a fault mode generation module, a fault detection module, a fault classification module, an inspection feature optimization module, and an abnormal prediction and evaluation module. In the present invention, by collecting multi-dimensional operating parameters of power equipment, analyzing the signal amplitude distribution and frequency domain changes, the comprehensiveness of equipment status perception is optimized. Combining the dynamic feature range and signal distribution deviation matching, potential fault features are accurately identified, and the accuracy of signal anomaly detection is improved. By extracting the differential characteristic values of current and temperature signals, the fault type classification is optimized, and the positioning ability of the problem root cause is enhanced. Dynamically extracting and matching signal trends, generating an inspection parameter matrix, and realizing global dynamic monitoring of the operating state. Based on the prediction model of signal change rate and time node range, the real-time performance and accuracy of abnormal trend prediction are improved, and the reliability of equipment operation and maintenance is enhanced.

The invention provides an electric blanket heating wire state judgment method and system, a medium and a product, and relates to the technical field of safety monitoring. In the method, a control processing unit firstly samples at a first preset frequency and calculates a signal change rate; and when the signal change rate value exceeds the standard rate threshold value, the control processing unit judges that the electric blanket is in a suspected heat accumulation state, and at the moment, the output power of the heating wire is controlled to be reduced to a preset duty ratio. Then, the control processing unit adopts a second preset frequency to collect signals and filter the signals, extracts signal values at the first moment and the second moment, and calculates the characteristic value of the thermal attenuation trend; and finally, the control processing unit realizes the distinguishing between the local wire twisting state and the folding state based on the deviation degree of the characteristic value and the standard thermal attenuation characteristic value. According to the method, the technical problem that the high-temperature cause cannot be distinguished only through the instantaneous temperature is relieved, and accurate recognition of the twisted wire fault and the folding working condition is achieved.

The embodiment of the invention provides a chip, a communication method and a consumable box, and the chip is used for determining to start to execute instruction information when a clock signal is changed from a continuous first level signal to a first level signal and a second level signal which are alternated; receiving first type instruction information or second type instruction information sent by the image forming device; if the instruction information is the first type of instruction information, determining a target reference moment based on the received clock signal; if it is not detected that the clock signal is changed from the first level signal to the second level signal within a preset target duration after a target reference moment, it is determined that execution of the instruction information is ended; if the instruction information is the second type of instruction information, it is determined that execution of the instruction information is ended at the ending moment of the second type of instruction information, and the ending moment is determined by the content of the second type of instruction information. The accuracy of data communication is improved.

The present disclosure relates to a delay module for a CAN device. The delay module is configured to delay only a single signal change of an RXD signal, wherein the single signal change forms the end of an idle state. Use of the delay module allows a second RXD signal to be generated. The present disclosure further relates to a CAN device comprising two CAN controllers. Each of the two CAN controllers may be provided with one of the two RXD signals. The CAN device may further be configured to detect possible errors based on the decoded bits of the RXD signals. As a result, the CAN device can communicate error-free with both distantly located further CAN devices and closely located further CAN devices, while ensuring a high data rate. The present disclosure also relates to a method for the delay module.

The invention provides an ultrasonic guided wave probability imaging method for structural damage in a temperature change environment, an improved U-Net neural network is constructed, input data of the improved U-Net neural network comprises an original guided wave signal and normalized temperature offset, and a model comprises a three-stage down-sampling module, a double-convolution layer and a three-stage up-sampling module. The attention mechanism module is connected behind the downsampling blocks of all levels and used for dynamically adjusting the feature weight, the representation capability of temperature features is enhanced, and the network can learn nonlinear mapping of temperature changes and signal changes. After model training data are obtained, filtering processing is carried out on original ultrasonic guided wave signals, normalized temperature deviation processing is carried out on temperature data, and the ultrasonic guided wave signals at the standard temperature are output through model prediction, so that the amplitude and phase interference of the temperature on ultrasonic guided waves is reduced, and the accuracy of ultrasonic guided wave detection is improved. And high-precision positioning of structural damage is realized in combination with a probability imaging technology. The method is small in data demand and few in execution steps, and the damage positioning efficiency is better than that of a traditional method.

The invention relates to a grabbing manipulator positioning device and method for quickly assembling and disassembling a drill, and belongs to the technical field of coal mine machinery design. The device comprises a drill rod transfer device, a drill rod box, a grabbing manipulator, a mounting frame, a driving motor, a PLC (Programmable Logic Controller), an induction block, a proximity switch sensor and the like. The sensing block is aligned with the corresponding device or the drill rod groove in position, and the proximity switch sensor is arranged below the driving motor. During working, the PLC controls the driving amount of the driving motor according to the signal change of the proximity switch sensor. The grabbing manipulator is driven to move, sensor signals are monitored, the driving amount of the driving motor is controlled according to the number of times, speed reduction and precise stopping are conducted when a target is approached, and drilling rod grabbing or releasing is completed. By means of the mode, rapid and accurate positioning can be achieved, the positioning speed and precision are improved, the drilling and unloading efficiency is improved, the operation stability is improved by adopting an intelligent control strategy, and the system flexibly adapts to different drill rod groove numbers and is high in universality and expandability.

The present application provides a method and system for monitoring and identifying the loosening of fan bolts based on acoustic emission technology. The method includes: collecting the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information, extracting signal change characteristic data according to the acoustic emission signal monitoring information, processing to obtain the signal theoretical change index, extracting fan condition change characteristic data according to the fan operation condition monitoring information, processing to obtain the fan operation dynamic quantity index, extracting environmental characteristic data according to the operation environment monitoring information, processing to obtain the environmental interference compensation coefficient, processing according to the signal theoretical change quantity index, the fan operation dynamic quantity index and the environmental interference compensation coefficient, obtaining the signal actual change quantity index, and judging the loosening of the target fan bolts, thereby realizing the technology of monitoring and identifying the loosening of fan bolts based on acoustic emission technology.

The invention discloses a laser intelligent management system of a laser particle analyzer, particularly relates to the technical field of laser control, and is used for solving the problem that pollution monitoring is not accurate due to intermittent work of a laser when an existing laser particle analyzer is in an energy-saving mode. By synchronously acquiring a laser working state signal and a background scattered light signal of an optical window, whether the laser is in an initial stage awakened from a low-power-consumption state is judged, and a nonlinear response stage dominated by pollutant state change in the background scattered light signal is identified; analyzing the symmetry of the signal change track at the stage to generate a symmetry breaking index, and synchronously executing Granger causal relationship test on the driving current and background scattered light signal time sequence data to obtain a causal relationship index; and distinguishing signal abnormal sources based on a symmetry breaking index and a causal relationship index so as to evaluate the current pollution level of the optical window, adaptively adjusting pollution monitoring parameters and controlling the working mode of a laser.

The invention relates to the technical field of laser radar signal processing, and discloses a laser radar echo signal processing method. The method comprises the following steps: acquiring an original echo signal sequence of a plurality of scanning periods, wherein the sequence comprises signal intensity data of all detection channels at each timestamp; comparing the signal intensity change mode of each detection channel in each scanning period with the signal intensity change mode of all detection channels in all scanning periods, and extracting the signal fluctuation index of each detection channel in each scanning period; analyzing a signal intensity distribution interval and a signal fluctuation index of each detection channel in each scanning period, and generating a signal dynamic variation; then comparing signal space distribution characteristics of each detection channel and all the rest detection channels in each scanning period and signal intensity gradient differences of adjacent channels, and extracting a space correlation difference; and fusing the signal dynamic variation of all the detection channels and the spatial correlation difference to generate a signal quality evaluation value of each scanning period.

The invention discloses a reaction capability evaluation method, head-mounted display equipment and a computer readable storage medium, and relates to the technical field of head-mounted display equipment, the method is applied to the head-mounted display equipment, and the method comprises the following steps: through a prompt output module, outputting prompt information corresponding to at least one reaction test action, and determining prompt output time; through a bio-electricity signal detection module, bio-electricity signal change caused by the fact that the wearer executes the reaction test action is detected in the target body surface area, and signal change time is determined; action completion time is determined through a reaction test action detection module; based on the prompt output time and the signal change time, the cognitive response ability of the wearer is evaluated, and a cognitive response ability evaluation result of the wearer is obtained; and based on the signal change time and the action completion time, evaluating the execution response capability of the wearer to obtain an execution response capability evaluation result of the wearer. According to the invention, refined layered evaluation of the reaction capability can be realized.

The invention discloses an automatic fault prediction method and system for operation and maintenance of a base station, and relates to the technical field of operation and maintenance of the base station, and the method comprises the steps: collecting a plurality of signal features at a plurality of monitoring positions of a communication base station, carrying out the change analysis of the signal features of a base station antenna, and obtaining a plurality of signal change parameters; performing base station antenna offset analysis according to the plurality of signal change parameters to obtain a plurality of antenna offset parameters, and performing offset credibility analysis to obtain a plurality of offset credibility; acquiring environment parameters in an environment where the communication base station is located, and performing base station antenna influence offset analysis in combination with the plurality of antenna offset parameters to obtain a plurality of antenna influence offset parameters; and according to the plurality of offset credibility and in combination with the plurality of antenna influence offset parameters, calculating to obtain an antenna fault scale, and taking the antenna fault scale as a base station operation and maintenance fault prediction result. According to the invention, the technical problem of poor fault prediction accuracy in operation and maintenance of the base station in the prior art is solved.

A power transmitter (101) provides power to a power receiver (105) via an inductive power transfer signal which is also used by the power receiver (105) for communication by load modulation. The power receiver (105) comprises a transmitter (509) arranged to transmit symbols in accordance with a first or second communication mode where the first mode modulates data symbols by a chip sequence from a set of sequences and the second communication mode does not. The power receiver (105) may transmit data symbols requesting a power transfer signal variation using a chip sequence from the set. A detector (515) may detect if the variation occurs, and if so a selector (513) selects the first communication mode and otherwise it selects the second communication mode. The approach may allow selection of an appropriate communication mode without requiring data to be transmitted from the power transmitter. The approach may provide improved backwards compatibility.

The invention relates to a method for detecting a defect in a structure using high-frequency mechanical waves. This method comprises: - acquiring (216) first signals S1,i measured by a first sensor and second signals S2,i measured, in parallel, by a second sensor, then - identifying (222), via a method of statistical analysis, in each signal S1,i, variations in this signal S1,i that are correlated with variations in the signal S2,i and, in each signal S2,i, variations in this signal S2,i that are correlated with variations in the signal S1,i, then - removing (230), from each of the signals S1,i and S2,i, variations in the signal that are identified as being correlated with variations in the other signal, to obtain signals Sc1,i and Sc2,i corrected for environmental variations common to the first and second sensors, then - detecting (250) defects based on the corrected signals Sc1,i and Sc2,i.

The invention provides an analog correlation circuit of a synthetic aperture radiometer receiver, and relates to the technical field of weak signal detection and processing. The circuit comprises a high-stability power supply reference unit, a high-performance broadband multiplication unit, a low-noise amplification unit and a high-linearity differential active filtering unit, wherein a second-order differential active filter in the high-linearity differential active filtering unit is used for carrying out noise filtering and low-noise amplification on a low-noise amplifier output signal in a differential form, and a differential signal of which the bandwidth is matched with the variation range of the amplitude of a target radiation brightness temperature signal and the amplitude meets the subsequent analog-to-digital conversion requirement is output. The high-linearity differential active filtering unit designed by the invention is realized by a second-order differential active filter formed by connecting two differential active filters in series, a low-pass filter with the cut-off frequency as low as possible filters noise in a link to the maximum extent, only the bandwidth matched with signal change is reserved, the amplitude which is two times of that of a single-ended signal can be provided, and the filtering efficiency is improved. And the linearity and the signal-to-noise ratio performance are better.

The invention relates to the technical field of electrostatic dust collection and discloses an accurate frequency adjustment method for an intelligent frequency modulation high-voltage power supply of an electrostatic dust collector. The method comprises the steps of collecting voltage signals with frequency fluctuation in operation of a high-voltage power supply to form a voltage sequence; dividing period segments by using wave crest distribution in the voltage sequence, and clustering to obtain adjustment subsequences; obtaining a trend fluctuation index based on local signal change trend fluctuation and signal differences of the adjustment subsequences, and obtaining an amplitude drop difference and an amplitude interval difference according to an amplitude change trend difference and an amplitude bit sequence change trend difference; obtaining a period stability coefficient by combining the difference, the average condition of the trend fluctuation index and the average condition of the sub-sequence similarity; constructing a Hankel matrix and performing singular value decomposition, and obtaining a main component proportion and a difference index based on a singular value condition; and according to the Hankel matrix line data correlation, the period stability coefficient, the main component proportion and the difference index, obtaining a signal stability index, and according to the signal stability index, accurately adjusting the high-voltage power supply frequency.

The invention discloses a multi-scene signal data-oriented multi-block mixed expert radio frequency fingerprint identification method, and belongs to the field of software radio. According to the implementation method, a plurality of expert modules are adopted, and each expert module is designed to be used for extracting feature representations under different conditions so as to cope with signal changes under various complex scenes. A lightweight and efficient route selection module is adopted to dynamically distribute and input signals to the most matched expert module according to the dimensionalities such as statistical characteristics, frequency domain structures or time sequence characteristics of the input signals, or output results are weighted and fused among a plurality of experts, so that the pertinence and flexibility of feature extraction and recognition are improved. A cooperative mechanism between expert modules is optimized by adopting a joint training strategy, so that an individual recognition model can maintain the independent expression ability of experts, and the convergence speed and stability of the whole model are improved by sharing bottom-layer expression and high-layer decision-making. The method is suitable for the field of software radio, and high-precision radiation source individual identification is realized.

The invention relates to the technical field of sensor multi-node calibration, in particular to a new energy automobile gear shifting position sensor multi-node calibration method based on the Internet of Things, and the method comprises the steps: obtaining a gear signal of each sensor in a monitoring time period; acquiring a gear signal sequence; acquiring a signal change rate of a single gear signal sequence; classifying each gear signal sequence according to the gear of each gear signal sequence and the gear of the previous gear signal sequence on the time sequence; acquiring a signal consistency factor of the single sensor in the single category according to the difference between the gear signal sequences of the single sensor in the single category; acquiring the signal stability of each sensor; the dynamic weight coefficient of each sensor is obtained by combining the difference degree between the gear signal sequence of each sensor and the gear signal sequence of other sensors; and the gear signal of each sensor is calibrated. According to the method, the calculation precision of the dynamic weight coefficient is improved, so that the multi-node calibration precision of the sensor is improved.

The chain vector results are analyzed to identify a faulty scan chain in the circuit and a fault associated with the faulty scan chain. The scan vector results are analyzed to identify one or more faulty scan element candidates on the faulty scan chain. An analog simulation is then performed to identify a faulty component candidate of the one or more faulty scan element candidates. During the process, a component of each of the one or more faulty scan element candidates that is sensitive to a change in signal at an input of each of the one or more faulty scan element candidates may be determined. Physical failure analysis may be performed on the circuit to locate one or more defective components in the faulty component candidates.

A RF generator includes a RF power source configured to generate an output signal at an output frequency, and an extremum-seeking frequency controller configured to generate a frequency control signal. The frequency control signal varies the output frequency of the RF power source, and the frequency control signal is formed from a gradient signal. The RF generator further includes a gradient estimator configured to generate the gradient signal. A frequency of the frequency control signal is adjusted based on the gradient signal. The gradient estimator is configured to receive frequency values of the frequency control signal and corresponding output response values, and the gradient signal is generated based on the frequency values and the output response values. Other example RF generators, methods for extremum-seeking frequency control of a RF generator, and control systems for controlling a RF generator are also disclosed.

According to the infrared combustible gas sensor and the method thereof, firstly, original voltage signals of a measurement channel and a reference channel and original signals of a temperature sensor are subjected to digital filtering, basic concentration is calculated based on the voltage of the measurement channel and the voltage of the reference channel after filtering, and dynamic impact detection is carried out; obtaining a reference channel signal change rate; furthermore, a self-adaptive dynamic compensation gain is introduced, the gain is dynamically adjusted according to the reference channel signal change rate and the filtered reference channel voltage, and the basic concentration is comprehensively compensated by combining the filtered temperature, so that unbalance caused by thermal shock in the photoelectric system is reflected more directly and more quickly. Therefore, the measurement precision and the data reliability of the infrared combustible gas sensor in a complex and changeable environment can be remarkably improved, and more accurate and more robust effects of compensation can be realized under different working conditions.