1773 results about "Leak detection" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention belongs to the technical field of leakage detection, and particularly relates to a leakage detection method based on multi-sensor time shift correction and deep learning. A plurality of sensors sensitive to leakage sound waves or vibration are arranged at different positions of a monitored pipeline or container and used for collecting leakage related signals; time alignment is carried out on the time sequence signals collected by the multiple sensors according to the position difference of the time sequence signals in the space; comparing and differentiating the plurality of sensor signals subjected to time alignment; modeling and updating environmental noise in real time, dynamically adjusting detection parameters according to noise level, continuously monitoring statistical characteristics of background signals, and establishing a noise model; a pre-trained deep learning model is used to carry out mode identification and classification determination on the signal after noise reduction processing; and the alarm and display module is used for triggering alarm and recording leakage information when suspected leakage is detected. According to the invention, weak leakage signals can be reliably detected under the condition of strong background noise.

Systems, methods, and devices for automated leak detection and resolution in a beverage dispensing system. A first signal associated with a peak dispensation pressure during beverage dispensation may be received, and after a time threshold, a second signal associated with a recovery dispensation pressure may be received. A malfunction, such as a leak, may be determined based on a difference between the first signal and the second signal exceeding a predetermined threshold. Automated reseating of the dispensation valve may be initiated to resolve the malfunction. Continuous monitoring and automated correction of leaks without requiring manual intervention are provided, improving reliability and reducing operational downtime in beverage dispensing systems.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention discloses an intelligent ground mobile high-precision gas leakage inspection and source tracking system, and relates to the technical field of gas pipe network leakage detection and leakage point positioning analysis. The system is mainly composed of a mobile inspection platform, a mid-infrared laser detection device, a three-dimensional anemograph, an inertial navigation module, a GIS system, a GNSS navigation positioning system, a wireless communication module and an intelligent detection comprehensive platform, and multi-source information fusion calculation, a dynamic adaptive analysis model and a maximum likelihood estimation method are combined. According to the intelligent gas leakage detection system, autonomous inspection can be achieved in a complex environment, abnormal gas component data can be processed in real time, detected gas space-time distribution information is transmitted to an intelligent comprehensive platform through a wireless communication module, data sharing and monitoring are completed, and efficient detection and accurate positioning of gas leakage are achieved. The method is suitable for gas leakage monitoring and component tracking in the fields of urban gas pipe networks, industrial parks and environmental protection, and the efficiency and precision of gas detection are remarkably improved.

The invention discloses a VOC gas leakage intelligent inspection system and method based on gas cloud imaging, and belongs to the technical field of VOC gas leakage detection. The inspection system comprises a moving track and an inspection point which are arranged along a monitored object, an intelligent sliding rail type robot which moves along the moving track, and a data processing system. The intelligent sliding rail type robot is provided with a gas cloud imaging host, and the gas cloud imaging host is provided with a medium wave infrared machine core, a visible light machine core and a wireless communication module. The intelligent slide rail type robot moves to each inspection point along the moving track, and the gas cloud imaging host shoots infrared images and visible light images of each inspection point and transmits the infrared images and the visible light images to the data processing system through the wireless communication module. According to the invention, a specific leakage point can be timely and accurately locked, the VOC leakage concentration can be determined, and full-process intelligent inspection from inspection, leakage detection processing, leakage accident archiving, leakage early warning, leakage accident processing to processing result feedback can be realized.

The invention discloses a blockage detection method, system and equipment for a flow measurement device and a medium, and relates to the field of blockage detection. The method comprises the following steps: acquiring an original pressure signal and an original flow signal of a to-be-measured flow measuring device, and generating an intrinsic mode function according to the original pressure signal and the original flow signal; extracting a time vector, a space vector and a frequency domain vector of the intrinsic mode function, obtaining an attention weight through a double-path cross attention mechanism, and performing weighted fusion on the time vector, the space vector, the frequency domain vector and the attention weight to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into a pre-trained blockage probability prediction model based on deep learning to obtain a blockage probability value; and constructing a state space according to the blockage probability value, the historical maintenance record and the equipment operation duration, balancing false alarm and leak detection punishment in combination with a reward function, and determining execution operation. By implementing the technical scheme provided by the invention, the accuracy and reliability of blockage detection are improved.

The invention relates to the technical field of energy storage, and discloses a fused salt heat storage tank leakage detection device which comprises a multi-mode sensing module used for monitoring temperature, conductivity and heat radiation changes caused by internal and external leakage of a heat storage tank in real time; the intelligent data processing module is used for receiving the multi-modal sensing data, performing fusion analysis and identifying leakage positions and severity; the leakage self-healing module is used for automatically blocking a leakage point and isolating a leakage area; the alarm module is used for providing sound-light alarm and information display of leakage; the data transmission and remote monitoring module is used for transmitting the detection data and the alarm information to a remote monitoring platform for real-time monitoring and maintenance; and the power supply and heat dissipation module is used for providing power support for the device and maintaining a stable operation environment. According to the invention, the device employs a multi-mode fusion scheme of optical fiber temperature sensing, conductivity sensing and infrared imaging technologies, and greatly improves the precision and coverage range of leakage detection.

The invention discloses a pipeline leakage detection device and method based on multi-modal data fusion, and relates to the technical field of pipeline safety monitoring. According to the device, modular sensor cabins (ultrasonic waves, LiDAR, millimeter wave radar and optics) are adopted for cooperative work, and high-precision leakage detection and positioning are achieved in combination with space-time registration, a cross-modal feature alignment network and a multi-level decision fusion mechanism. Through a dynamic weight distribution model and a multi-physical field joint inversion algorithm, the problems of low detection precision and poor anti-interference capability of a traditional single sensor are effectively solved. The method is suitable for detecting defects such as corrosion and leakage of metal and nonmetal pipelines, and has the advantages of high sensitivity, strong robustness and wide applicability.

According to the boiler tube wall thinning detection method based on multi-modal sensing fusion, multi-source data synchronous acquisition and deep fusion of a millimeter wave radar, an infrared thermal imager, a laser radar and a visible light camera are combined with a dynamic Bayesian optimization scanning strategy and a four-channel convolutional neural network classification algorithm; according to the invention, high-precision identification and full-coverage monitoring of boiler tube wall thinning defects are realized, the problem of leak detection of a traditional single sensor under complex working conditions is solved, furnace shutdown or manual intervention is not needed, the detection efficiency is improved, and safe, reliable and intelligent-decision nondestructive detection technical support is provided for high-risk scenes such as thermal power generation and the like.

The invention discloses an indoor gas leakage monitoring and intelligent joint control system, and belongs to the technical field of intelligent safety monitoring systems. Comprising a sensing layer, a network layer, a control layer and a platform layer, the sensing layer comprises a multi-sensor array and an edge computing node, the multi-sensor array is combined with a catalytic combustion sensor, an infrared sensor, a semiconductor sensor and a temperature and humidity sensor, and sensor data are fused through a weighting algorithm so as to reduce the false alarm rate; a temperature and humidity sensor dynamically adjusts a detection threshold value to adapt to different climate conditions, and an edge computing node is deployed with a lightweight machine learning model. Through multi-sensor data fusion and a weighting algorithm, the false alarm rate is reduced to be smaller than 0.1% per year, environment self-adaptive calibration and AI auxiliary decision making are performed, interference of alcohol, perfume and the like is effectively eliminated, different climate conditions are adapted, leakage judgment is completed at an equipment end through edge calculation, cloud delay is avoided, a multi-stage emergency strategy is provided, and intelligent safe ecology driven by the Internet of Things is constructed.

The invention belongs to the technical field of fused salt heat storage tank leakage detection, and discloses a device for fused salt heat storage tank leakage detection and a working method thereof. According to the device for detecting the leakage of the fused salt heat storage tank, a rotary supporting assembly is rotatably mounted on a base, and an equipment mounting frame is further mounted on the base; a plurality of spray heads and a plurality of cameras are arranged on the equipment mounting rack; one end of the detection cylinder is provided with a detection pipe and an air inlet pipe, and the other end is provided with an air outlet; one end provided with the air outlet is provided with a distance sensor, and the other end provided with the air outlet is further provided with a piston plate through an elastic assembly; the control unit is used for controlling rotation of the rotary supporting assembly, controlling opening and closing of the spray head, obtaining data collected by the distance sensor and obtaining data collected by the camera. According to the technical scheme, bubble visual monitoring and pressure change monitoring are combined, and the sensitivity and accuracy of leakage detection are improved.

The invention relates to the technical field of pressure vessel detection, in particular to a pressure vessel leakage detection device which comprises a transversely-arranged detection barrel, a leakage detection mechanism suitable for sealing detection of various types of pressure vessels is arranged on the detection barrel, and the leakage detection mechanism comprises movable ring bodies arranged at the two ends of the inner side of the detection barrel in a sliding mode respectively. The movable ring body is fixed in the middle of the inner side of the detection barrel, the detector bodies are fixedly installed on the movable ring body and the limiting ring body respectively and arranged in a circumferential array mode, and a positioning assembly capable of limiting the positions of pressure containers of different sizes is arranged on the inner side of the detection barrel. According to the invention, the detection device can detect the sealing performance of containers with different sizes more conveniently, the accuracy of the detection result of the detection device is improved, the application range of the detection device is expanded, the situation that the detection result is easy to have errors when the pressure containers with different sizes are detected by the existing detection device is avoided, and the detection efficiency is improved. And the working efficiency of the detection device is improved.

A system of evaluating a mobile device includes a sensing device that captures data while a force is being applied to a display of the mobile device while the mobile device is held stationary; and a processing device to process the data and determine the force applied and a displacement of the display while the force is being applied.

A leak detection system includes a leak detection apparatus. The leak detection apparatus includes an electrode mesh and an insulative layer associated with the electrode mesh. The insulative layer is engageable with a conducting fluid containing component. The electrode mesh is configured to output an electric signal responsive to a conducting fluid reaching the electrode mesh through the insulative layer. The leak detection apparatus further includes a computing device configured to receive said electrical signal and associate said electrical signal with a leak event by correlating said electrical signal with a baseline electrical output of the electrode mesh.

A roofing system configured for electronic leak detection, including: (a) a roof deck; (b) an insulation board or a coverboard positioned above the roof deck; (c) a layer of conductive material positioned above the insulation board or the coverboard; and (d) a roofing membrane positioned above the layer of conductive material. The layer of conductive material is pre-attached at a factory onto one of: (i) the top side of the insulation board or the coverboard, or (ii) the bottom side of the roofing membrane, such that there is only one layer of conductive material in the roofing system.

Methods and systems for an air conditioning unit is provided. The air conditioning unit includes a housing having a first compartment and a second compartment separated from the first compartment by a partition disposed in the housing, the housing including an air inlet and an air outlet. A refrigerant circuit includes a compressor disposed in the first compartment. An air flow path extends through the second compartment. The air flow path is arranged to route air flow from the air inlet to the air outlet, and a blower is disposed in the air flow path. A refrigerant leak detection system is configured to detect a refrigerant leak within the housing. An inducer is disposed in the housing. The inducer is configured to move air from the first compartment into the air flow path upon detection of the refrigerant leak by the refrigerant leak detection system.

The invention belongs to the technical field of safety monitoring, and particularly relates to a liquid leakage detection alarm system based on optical detection and a method thereof. Comprising a plurality of liquid leakage monitoring alarm terminals, a plurality of portable comprehensive monitoring communication devices, a fixed comprehensive monitoring communication device, and necessary auxiliary devices such as a system server and a comprehensive alarm unit. The liquid leakage monitoring alarm terminal is internally provided with a liquid accumulation tank, and light emitted by a light emitting end in the liquid accumulation tank is received by a light receiving end. When a liquid leakage event occurs, leaked liquid flows into the liquid accumulation groove, the light receiving end cannot receive the light emitted by the emitting end, an output signal of the optical detection module changes, and the liquid leakage monitoring alarm terminal gives a liquid leakage sound-light alarm. And wireless signals are sent to the portable comprehensive monitoring communication machine and the fixed comprehensive monitoring communication machine to carry out remote alarm. The liquid leakage detector is suitable for leakage detection of all liquids, and has the outstanding advantages of simple structure, high reliability, high safety, high sensitivity, miniaturization, easy integration and the like.

The invention relates to the field of vanadium redox flow batteries, and discloses a vanadium redox flow battery galvanic pile assembly leak hunting method and system, and the method comprises the following steps: obtaining a reference fluid and chemical parameter distribution in a galvanic pile through constructing a multi-level fluid mechanics model and a chemical reaction kinetic model; collecting and monitoring chemical parameters such as pressure, flow, vanadium ion concentration, oxidation-reduction potential and pH value in the galvanic pile in real time; and performing multi-dimensional feature fusion on the acquired data to generate a data matrix, performing abnormal mode recognition on the data matrix by using a deep learning model of a convolutional neural network and a long-short term memory network, and accurately positioning a leakage position. According to the invention, comprehensive monitoring of fluid and chemical parameters in the galvanic pile is realized, detection requirements of tiny leakage can be rapidly responded, high precision and real-time performance are realized, and effective technical support is provided for safe operation of the vanadium redox flow battery.

The invention relates to the technical field of pipeline leakage detection, in particular to a pressure-bearing pipe leakage detection method, which is characterized by comprising the following steps of multi-mode signal active excitation, topological vibration mode adaptive modeling, intelligent signal fusion and denoising, accurate leakage positioning, leakage grade classification and intelligent early warning. According to the method, multi-mode signal active excitation, topological vibration mode self-adaptive modeling and intelligent signal fusion and denoising technologies are utilized, the limitation that a traditional method only depends on a single parameter and complex environment leakage signals are difficult to separate is broken through, and the method can adapt to working condition changes such as temperature, pressure and flow velocity in real time, so that the detection accuracy and reliability are remarkably improved; and meanwhile, through multi-sensor combined positioning, multi-modal data time sequence fusion and a leakage level classification and early warning mechanism based on fuzzy logic and LSTM, accurate calculation and timely alarm of a leakage position are realized, the misjudgment and leak detection risks are effectively reduced, and the safe operation and maintenance efficiency of industrial equipment is improved.

The utility model discloses a liquid leakage detection structure and a motor controller, the liquid leakage detection structure comprises a housing, a sealing member, a heat conduction plate and a detection assembly, a water channel for cooling liquid to flow is formed in the housing, and the outer wall surface of the housing is provided with an opening communicated with the water channel and an annular groove surrounding the opening; partial structure of the sealing piece is limited in the annular groove; the heat conducting plate is connected with the shell, covers the opening and shields the sealing piece so as to abut against the sealing piece to seal the opening; the detection assembly comprises a detection piece and a circuit board, a detection pin of the detection piece extends into the annular groove and extends to the side, away from the opening, of the sealing piece, and the circuit board is electrically connected with the detection piece. The utility model aims to reduce the omission ratio of cooling liquid leakage detection and avoid the consequences seriously endangering personal safety, such as short circuit, sparking and the like, caused by liquid leakage.

The invention discloses a water supply network leakage detection method based on time-frequency domain characteristics of a ground penetrating radar, and relates to the technical field of leakage detection. The method comprises the steps that echo signals, collected by the ground penetrating radar, of different areas of the water supply pipe network and leakage detection results of all the areas are obtained; the leakage detection results comprise non-leakage, leakage and leakage positions; extracting time-frequency domain characteristics of the echo signals; performing feature screening in the time-frequency domain features by adopting a random forest algorithm and a principal component analysis method to obtain effective features of the echo signals; forming a training data set by the effective features of all the echo signals and the leakage detection results of different areas; training a back propagation neural network by adopting the training data set; and inputting effective characteristics of echo signals of the water supply network to be detected, which are acquired in real time, into the trained back propagation neural network, and outputting a leakage detection result of the water supply network to be detected. The accuracy and efficiency of leakage detection of the water supply network can be improved.

The invention relates to the technical field of data processing, in particular to a vacuum box helium leak detection equipment data processing method. The method comprises the following steps: acquiring a helium concentration signal, filtering the helium concentration signal to obtain a preprocessed concentration signal, setting a reference point of each data point, constructing a reference point set of each data point, and calculating the credibility of the reference points in the reference point set; determining the confidence weight of each reference point based on the credibility of the reference points; and taking the product of each confidence weight and the preprocessed concentration data of the corresponding reference point as a local contribution value, and taking the sum of a plurality of local contribution values as a final filtering result. The helium concentration data processing method and device have the effect of improving the helium concentration data processing accuracy.

The utility model relates to a leak detection clamp for automobile parts, which comprises a lower clamp, an upper clamp and an air inlet mechanism, an object to be detected comprises a cavity to be detected, a channel to be detected is arranged in the cavity to be detected, and the cavity to be detected is further provided with an opening communicated with the channel to be detected. The upper clamp is provided with a first blocking piece capable of blocking the outer end of the to-be-detected channel, a blocking mechanism used for blocking the inner end of the to-be-detected channel is additionally installed on the sealing seat, the blocking mechanism comprises a head end and a tail end, and when the first blocking piece blocks the outer end, the first blocking piece drives the head end to move towards the tail end at the same time. According to the technical scheme of the utility model, under the condition that the outer end of the to-be-detected channel is plugged by the first plugging member, the plugging mechanism at the inner end of the to-be-detected channel can be plugged at the same time. Therefore, the leak detection clamp can be suitable for channels which are communicated with other openings and need to be tested independently.

The invention provides a steel shell concrete interface void detection system and method, and relates to the technical field of civil engineering structure health detection, and the system comprises a mobile scanning unit, an excitation unit, an infrared imaging unit, a signal processing and acquisition unit and a comprehensive analysis terminal. The method comprises the following steps: controlling a mobile scanning unit to carry out step-by-step impact excitation and signal acquisition so as to obtain a frequency diagram, and synchronously sampling by using an infrared thermal imager so as to obtain a thermal analysis diagram; and then binarizing the two images respectively, fusing the two images by adopting a noise suppression algorithm to generate a comprehensive image, and judging that the interface is void according to the fused image. According to the method, the high sensitivity of the impact echo method to the layering defect and the accurate description capability of the infrared thermal imaging method to the defect shape are fused, so that nondestructive, efficient and high-precision detection of the steel shell concrete interface void is realized, and the misjudgment and leak detection risks of a single method are effectively reduced.

The utility model discloses a stop valve leak detection tool which comprises an operation table and a stop valve body, a fixed box is fixedly installed on the right side of the top of the operation table, a sliding box is connected to the left end of the top of the operation table in a sliding mode, and a plurality of liquid outlet pipes are symmetrically installed on the opposite sides of the fixed box and the sliding box. The liquid outlet pipe is communicated with an inner cavity of the fixed box and an inner cavity of the sliding box, a plurality of stop valve supporting blocks are arranged between the sliding box and the fixed box, and the stop valve supporting blocks slide on the top of the operation table. The stop valve body is placed on the top of the stop valve supporting block, the liquid outlet pipe is connected with the stop valve body through sliding of the sliding box, the bright color leakage detection liquid is injected into the inner cavity of the stop valve body through the water pump, and whether the surface of the stop valve body leaks or not is observed so that the sealing performance of the stop valve body can be judged. The leak detection tool is simple in operation, can carry out leak detection operation on a plurality of stop valve bodies at the same time, and improves the efficiency.

The invention relates to a method for improving sensitivity of a pressure vacuum method helium mass spectrum leak detection system, and belongs to the technical field of nondestructive testing. Establishing a vacuum leak detection system; the vacuum chamber is connected with the calibration leak hole through a pipeline; putting the detected piece into the vacuum chamber, and closing a gate of the vacuum chamber; opening a mechanical pump valve and a molecular pump valve; vacuumizing the vacuum chamber through a mechanical pump and a molecular pump until the vacuum degree of the vacuum chamber is stable; starting the leak detector to enter a leak detection state; opening the leak detection valve to enable the leak detector to be communicated with the vacuum chamber; gas is input through the micro-flow device; after the number measured by the leak detector is stable, reading a background value I0 and a noise value In of the vacuum leak detection system; opening a calibration leak hole; after the number measured by the leak detector is stable, the reaction value I of the calibration leak hole is measured; calculating the effective minimum leak detection rate Qemin of the vacuum leak detection system, and completing the measurement of the sensitivity of the system; the pre-stage vacuum degree of the leak detection system is adjusted, so that the background and noise of the leak detection system are reduced, and the detection sensitivity of the system is improved.

The invention discloses an AMC sensor array spatial feature recognition and leakage point detection method based on deep learning, and relates to the technical field of sensors, and the method comprises the steps: carrying out the spatial correlation analysis of a preprocessing result, and extracting a spatial feature signal reflecting the spatial distribution characteristics of gas; decomposing the denoised spatial features by using an empirical mode decomposition algorithm to obtain a plurality of intrinsic mode components, and generating a reconstruction signal based on each intrinsic mode component; performing framing processing on the reconstructed signal by using a framing windowing technology, calculating an energy value and a zero-crossing rate of a corresponding frame based on a framing processing result, constructing a leakage sensing model in combination with the extracted spatial feature signal, and generating a leakage probability distribution sequence; and inputting the time sequence window sequence into a self-encoder, and performing gas leakage point detection in combination with a knowledge distillation mechanism and a comparative analysis technology. According to the invention, accurate extraction and de-noising processing of the spatial feature signals are realized, the signal quality is improved, and the detection precision is further improved.

A refrigeration system includes a tubing system, a sensor circuitry, a fan, and a controller. The tubing system has tubes located adjacent to component of the refrigeration system. The tubes have holes on their surface. The fan is downstream of the tubes. The fan pulls sampled air though the tubes and direct the sampled air toward the sensor circuitry. The controller receives a signal that indicates that the sampled air comprises a mixture of air and refrigerant particles. In response to receiving the signal, the controller determines that there is refrigerant leakage from at least one of the one or more components and triggers a mitigation plan associated with the refrigerant leakage.