A method and system for multiple fault diagnosis of a sheath flow pressure sensor

Abstract

This invention relates to the field of medical technology, specifically to a multi-fault diagnosis method and system for a sheath flow pressure sensor. The invention integrates multiple diagnostic dimensions, including average value change monitoring (equipped with both normal and multiplication modes, and employing a fast sorting algorithm to remove outliers and improve accuracy), abrupt change monitoring (based on robust calculation after continuous multi-point sampling and removal of high and low points), and maximum / minimum pressure monitoring (based on continuous multi-point judgment). A comprehensive judgment is then made based on a set threshold issued by a host computer, ultimately triggering differentiated Class A or Class B alarms. The sheath flow pressure sensor is connected in series with the optical sheath flow sensor at a very short distance, greatly improving real-time performance and accuracy. This method significantly enhances the accuracy, reliability, and intelligence of sheath flow pressure monitoring, providing a solid guarantee for the stable operation of blood analyzers.

Description

Technical Field

[0001] This invention relates to the field of medical technology, and in particular to a method and system for diagnosing multiple faults in a sheath flow pressure sensor. Background Technology

[0002] In blood cell analyzers, the sheath flow system is the core component ensuring accurate cell counting and classification, and the stability of its pressure directly determines the accuracy and reliability of the test results. The sheath flow pressure sensor acts as the "eyes" monitoring this system; abnormal fluctuations in its readings often indicate malfunctions such as flow path blockage, leakage, pump abnormalities, or sensor failure. Therefore, real-time and accurate fault diagnosis of the sheath flow pressure is crucial.

[0003] Currently, most common pressure monitoring solutions employ a simple single-threshold judgment, triggering an alarm only when the pressure value exceeds a fixed upper or lower safety limit. This traditional technical solution has the following inherent defects and limitations: Limited diagnostic dimensions and high false alarm and false negative rates: The single-threshold method cannot effectively distinguish between transient noise interference, trend drift, and substantial hardware failures. A brief pressure pulse (such as bubble interference) may trigger a false alarm, while slow performance degradation of the sensor (such as slow blockage) may not be detected in time because it has not reached the threshold, leading to false alarms and posing a hidden danger to the stable operation of the equipment. Lack of fault early warning and classification capabilities: Traditional methods only trigger alarms after a fault has fully occurred, failing to provide early warning information and making it difficult for maintenance personnel to implement predictive maintenance. Furthermore, it cannot differentiate the severity of faults and cannot trigger differentiated handling strategies (such as the difference between a prompt warning and immediate shutdown), reducing the intelligence of operation. Poor data resistance to interference and low benchmark reliability: Although some improvement schemes introduce multi-point sampling, the data processing algorithms are relatively simple (such as simple arithmetic average), which are easily affected by extreme outliers (outliers) in the sampled values, resulting in distortion of the diagnostic benchmark and unreliable judgment results.

[0004] Risks of direct connection between traditional devices and syringes: Direct connection between traditional devices and syringes poses a risk of syringe bursting and leakage. These problems are often hidden and difficult to detect in a timely manner through conventional monitoring methods. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for multiple fault diagnosis of sheath flow pressure sensors, aiming to solve the problem of high false alarm and false alarm rates in traditional pressure monitoring methods.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for diagnosing multiple faults in a sheath flow pressure sensor, comprising the following steps:

[0007] Real-time acquisition of pressure signals from the sheath flow system, followed by signal conditioning and analog-to-digital conversion;

[0008] The sampled data is subjected to equalization filtering to obtain filtered pressure data;

[0009] Determine whether the current pressure value exceeds the preset absolute safety threshold. If it does, trigger a Level A alarm and immediately shut down the machine. If it does not exceed the threshold, store the data in the buffer and perform detailed analysis after the sample collection is completed.

[0010] The sample data is quickly sorted, and after removing outliers at both ends, the robust middle segment is extracted.

[0011] Calculate the abrupt change and the average change of the robust segment. If the abrupt change or the average change exceeds the set threshold, a Class B alarm is triggered.

[0012] After completing the analysis of the current sample, clear the buffer and proceed to the next sample collection loop.

[0013] The abrupt change is obtained by calculating the mean absolute value of the first-order difference sequence, and is used to detect instantaneous pressure fluctuations.

[0014] The change in the average value is obtained by comparing the difference between the current average value of the sample segment and the historical average value, and is used to detect pressure trend drift.

[0015] The Class A alarm is used to respond to severe pressure exceeding the limit and trigger an immediate shutdown; the Class B alarm is used to respond to trend anomalies and trigger an audible and visual warning.

[0016] The equalization filter uses an arithmetic average of three consecutive samples to suppress high-frequency noise.

[0017] In a second aspect, a multi-fault diagnosis system for a sheath flow pressure sensor is used in the first aspect, the multi-fault diagnosis method for the sheath flow pressure sensor, comprising a sheath flow pressure sensor, a signal conditioning circuit, a microcontroller, an actuator, and a host computer.

[0018] The sheath flow pressure sensor is used to collect the pressure of the sheath flow system in real time and convert it into an analog electrical signal;

[0019] The signal conditioning circuit is used to amplify and filter the analog signal;

[0020] The microcontroller is used to receive the conditioned signal, execute multiple fault diagnosis algorithms, determine the alarm level, and output control commands.

[0021] The actuator is used to receive control commands and perform corresponding actions, including audible and visual alarms and pump and valve control.

[0022] The host computer is used to display pressure waveforms, alarm information, and configure diagnostic parameters.

[0023] This invention discloses a multi-fault diagnosis method for a sheath flow pressure sensor. The method integrates multiple diagnostic dimensions, including average value change monitoring (equipped with both normal and multiplication modes, and employing a fast sorting algorithm to remove outliers and improve accuracy), abrupt change monitoring (based on robust calculation after continuous multi-point sampling and removal of high and low points), and maximum / minimum pressure monitoring (based on continuous multi-point judgment). A comprehensive judgment is then made based on a set threshold issued by a host computer, ultimately triggering a differentiated Class A or Class B alarm. The sheath flow pressure sensor is connected in series with the optical sheath flow sensor at a very short distance, greatly improving real-time performance and accuracy. This method significantly enhances the accuracy, reliability, and intelligence of sheath flow pressure monitoring, providing a solid guarantee for the stable operation of blood analyzers. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a multiple fault diagnosis method for a sheath flow pressure sensor provided by the present invention.

[0026] Figure 2 This is a schematic diagram of a multi-fault diagnosis system for a sheath flow pressure sensor provided by the present invention.

[0027] Figure 3 This is a schematic diagram of an embodiment.

[0028] In the diagram: 101-Sheath flow pressure sensor, 102-Signal conditioning circuit, 103-Microcontroller, 104-Actuator, 105-Host computer;

[0029] 301-Sheath flow device front end, 302-Injector, 303-Delivery line, 304-Circuit line, 305-Signal processing unit, 306-Circuit board, 307-Sheath flow pressure sensor, 308-Sheath fluid delivery line, 309-Optical sheath flow module, 310-Optical sheath flow device. Detailed Implementation

[0030] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0031] Please see Figure 1 In a first aspect, the present invention provides a method for diagnosing multiple faults in a sheath flow pressure sensor, comprising the following steps:

[0032] S1 acquires pressure signals from the sheath flow system in real time and performs signal conditioning and analog-to-digital conversion.

[0033] Specifically, the sensor begins operation, acquiring analog pressure signals from the flow path system in real time and converting them into raw electrical signals for output. The raw electrical signals from the sensor are transmitted to signal conditioning circuit 102. This circuit includes operational amplifiers and filter networks to amplify, impedance match, and perform preliminary analog filtering on the weak sensor signals to improve signal quality and suppress some environmental noise. The conditioned analog signal is then sent to the analog-to-digital converter (ADC) module integrated within the microcontroller 103 (MCU). The ADC discretizes the analog signal into a digital signal at a fixed sampling frequency, enabling the MCU to perform subsequent digital signal processing and algorithm analysis.

[0034] S2 performs equalization filtering on the sampled data to obtain filtered pressure data;

[0035] The equalization filter uses an arithmetic average of three consecutive samples to suppress high-frequency noise.

[0036] Specifically, the data sequence obtained by ADC sampling is subjected to digital filtering. This embodiment of the invention employs a three-times superposition and averaging algorithm, that is, the arithmetic mean of three consecutive sampled values ​​is calculated to smooth random fluctuations and filter out high-frequency noise, resulting in a more stable and reliable pressure data point.

[0037] S3 determines whether the current pressure value exceeds the preset absolute safety threshold. If it does, it triggers a Class A alarm and immediately shuts down. If it does not exceed the threshold, it stores the data in the buffer and performs detailed analysis after the sample collection is completed.

[0038] The Class A alarm is used to respond to severe pressure over-limit and trigger an immediate shutdown.

[0039] Specifically, the filtered real-time pressure value is compared with the preset upper and lower limits of the absolute safety threshold, and a logical judgment is performed on the maximum and minimum values. This step is the first line of defense to ensure system safety, aiming to quickly identify serious over-limit faults. This is the judgment node (diamond box). It determines whether the current pressure value continuously exceeds the absolute threshold range determined by the system safety specifications. If the judgment is "yes", the highest level A error alarm is immediately triggered. The MCU will output control signals to execute the most stringent protection measures, such as immediate shutdown and closing of pump valves, to prevent the equipment from operating under dangerous over-limit pressure and to ensure the safety of the equipment and samples. If the judgment is "no", the valid pressure data points determined by the extreme value judgment are stored in the pre-allocated sample data buffer in the MCU memory to accumulate data for subsequent refined analysis of the entire sample. This is the judgment node (diamond box). It determines whether the current sample data volume has reached the predetermined number of sampling points and the sample counting has ended. If the judgment is "no", the process returns to perform digital filtering processing on the data sequence obtained by the ADC sampling. This invention employs a three-times superposition and averaging algorithm, which involves arithmetically averaging three consecutive sample values ​​to smooth random fluctuations and filter out high-frequency noise, resulting in a more stable and reliable pressure data point. This process continues with data collection and filtering to accumulate the next data point. If the determination is "yes," a detailed analysis routine is invoked to perform a comprehensive, computationally intensive diagnostic algorithm on the complete sample dataset already collected in the buffer, in order to identify trending faults and potential risks. The analysis of the collected complete dataset then begins.

[0040] S4 quickly sorts the sample data, removes outliers at both ends, and extracts the robust middle segment.

[0041] Specifically, the QuickSort algorithm is used to sort and rearrange the sample dataset. This step aims to efficiently rearrange the data, preparing it for subsequent removal of outliers and extraction of valid data segments.

[0042] S5 calculates the abrupt change and the average change of the robust segment. If the abrupt change or the average change exceeds the set threshold, a Class B alarm is triggered.

[0043] The Class B alarm is used to respond to trend anomalies and trigger audible and visual warnings.

[0044] The abrupt change is obtained by calculating the mean absolute value of the first-order difference sequence, and is used to detect instantaneous pressure fluctuations.

[0045] The change in the average value is obtained by comparing the difference between the current average value of the sample segment and the historical average value, and is used to detect pressure trend drift.

[0046] Specifically, based on the sorted data sequence, unnecessary values ​​at both ends of the sequence are removed (determined by the pressure distribution in the hydraulic system), thus obtaining a robust intermediate average data segment that truly reflects the core pressure change trend. Based on the obtained robust data segment, its abrupt change is calculated (e.g., calculating the absolute value or standard deviation of the first-order difference sequence) to quantify the instantaneous pressure fluctuation characteristics. The arithmetic mean of the robust data segment is calculated and compared with the average value of historical samples or the expected reference value to obtain the average change, used to quantify the slow drift or trend change of pressure. This is the judgment node (diamond box). It is determined whether the calculated abrupt change exceeds the abrupt change threshold issued by the host computer 105. If the judgment is "yes", the process enters the comprehensive judgment result, triggering the secondary priority B-level circuit alarm. The system issues an audible and visual warning, prompting the operator to pay attention to potential trend faults (such as blockage or leakage), but does not immediately shut down the system; one sample analysis ends. If the judgment is "no", it is determined whether the calculated average change exceeds the average change threshold issued by the host computer 105. If the judgment is "yes", the system will trigger a secondary priority Class B circuit alarm based on the overall judgment result. The system will issue an audible and visual warning to alert the operator to potential trending faults (such as blockages or leaks), but will not immediately shut down the system; one sample analysis will then conclude. If the judgment is "no", the process will proceed to the next step.

[0047] After S6 completes the analysis of the current sample, it clears the buffer and enters the next sample collection loop.

[0048] Specifically, the detailed analysis process for the current sample is completed without triggering any alarms. The system clears the sample buffer, and the analysis of one sample ends. The system resets the sample counter and related flags, prepares for and starts the acquisition and diagnosis loop for the next sample, and the process returns to performing digital filtering on the data sequence obtained from the ADC sampling.

[0049] Please see Figures 2 to 3 In a second aspect, a multi-fault diagnosis system for a sheath flow pressure sensor is used in the first aspect, the multi-fault diagnosis method for the sheath flow pressure sensor, comprising a sheath flow pressure sensor 101, a signal conditioning circuit 102, a microcontroller 103, an actuator 104, and a host computer 105.

[0050] The sheath flow pressure sensor 101 is used to collect the pressure of the sheath flow system in real time and convert it into an analog electrical signal;

[0051] The signal conditioning circuit 102 is used to amplify and filter the analog signal;

[0052] The microcontroller 103 is used to receive the conditioned signal, execute a multi-fault diagnosis algorithm, determine the alarm level, and output control commands.

[0053] The actuator 104 is used to receive control commands and perform corresponding actions, including audible and visual alarms and pump and valve control.

[0054] The host computer 105 is used to display pressure waveforms, alarm information, and configure diagnostic parameters.

[0055] In this embodiment, the sheath flow pressure sensor 101 is used to monitor the pressure of the sheath flow system in the blood cell analyzer in real time and convert it into an analog electrical signal. The signal conditioning circuit 102 is connected to the sheath flow pressure sensor 101 and is used to amplify, filter, and condition the analog electrical signal. Specifically, it amplifies the weak sensor signal to a suitable sampling amplitude and uses a filtering circuit to filter out high-frequency electromagnetic interference noise. The microcontroller 103 (MCU) is connected to the signal conditioning circuit 102 and is used to receive the conditioned signal and execute internally stored multiple fault diagnosis algorithms (including but not limited to abrupt value detection, average value change detection, and extreme value detection); make alarm logic decisions based on the algorithm results (such as determining it as a level A alarm or a level B alarm); and execute system control logic. The actuator 104 is connected to the microcontroller 103 and is used to receive the output instructions of the microcontroller 103 and execute corresponding operations, including but not limited to triggering an audible and visual alarm and controlling the pumps, valves, or motors of the sheath flow system to take corresponding actions (such as stopping or reducing speed). The host computer 105 (human-machine interface) is communicatively connected to the microcontroller 103, and is used to display real-time pressure waveforms, display alarm information, and allow users to configure various diagnostic threshold parameters. The microcontroller 103 sends pressure data and alarm status to the host computer 105, and simultaneously receives control commands and configuration parameters from the host computer 105.

[0056] Example:

[0057] It mainly consists of three parts: the sheath flow system front end, the signal processing unit, and the optical detection unit. Through innovative sensor layout and system connection, it solves the pain points of traditional solutions such as tube bursting, leakage, and lack of early warning.

[0058] The front end of the sheath flow device is the supply source of the sheath flow fluid, which usually includes components such as a precision pump, a buffer, and a control valve, used to generate and deliver a stable and controllable sheath flow fluid.

[0059] The syringe serves as a stable power source for the movement of the sheath fluid.

[0060] The main function of the delivery pipeline is to transport the sheath fluid to the core optical detection unit.

[0061] The circuit is the electrical wiring that connects the sensor to the circuit board, and is responsible for powering the sensor and transmitting its output analog electrical signals.

[0062] The signal processing unit (circuit control and processing section) and the circuit board serve as the control and data processing center of the system. The PCB integrates a microprocessor (MCU), ADC module, signal conditioning circuitry, and communication interfaces. It is responsible for receiving sensor signals, executing the aforementioned multiple fault diagnosis algorithm, and outputting control commands.

[0063] The sheath flow pressure sensor and the sheath fluid delivery pipeline are installed directly and adjacent to the inlet or outlet of the optical sheath flow device through the pipeline, so as to monitor the real pressure entering the optical sheath flow device in real time and directly.

[0064] The optical sheath flow module and optical sheath flow apparatus are the core detection units of the system. Sample cells are focused with the sheath flow fluid here and irradiated by a laser to generate optical signals for cell analysis. This part is the pressure-sensitive point and key terminal of the entire flow path system.

[0065] Sheath flow device front end (corresponding) Figure 3 (301-303): This module is the sheath fluid supply and initial delivery unit for the system. It integrates a precision injection pump and control valve to generate a stable and controllable sheath fluid. The generated sheath fluid is ejected through a syringe (302) and smoothly delivered to the downstream module through a dedicated sheath fluid delivery pipeline (303). The principle of this module is to provide a stable, pulse-free raw sheath flow for the entire system, and the resulting flow pattern is the basis for subsequent accurate pressure monitoring. By separating the functions from the downstream pressure monitoring module, this front end can focus on the stability of the flow source, providing a high-quality initial input for the system. The flow fluctuation coefficient at the outlet (303) of this module can be monitored by a flow sensor, thereby providing a reliable basis for subsequent accurate diagnosis.

[0066] Signal processing and control unit (corresponding) Figure 3(304-307): This module is the signal sensing, conditioning, and control center of the system. Sheath fluid flows from upstream through the delivery pipeline (303) into this module. The core component of the module is the sheath flow pressure sensor (307), which senses the fluid pressure in the pipeline (303) in real time and converts it into an analog electrical signal. This analog signal is transmitted to the signal conditioning circuit board (306) through a dedicated PCB connection line (304). The circuit board (306) is responsible for amplifying and filtering the signal (such as the aforementioned mean filtering). The conditioned standard signal is finally sent to the microprocessor (usually also integrated on the circuit board 306) for ADC sampling and execution of the multi-fault diagnosis algorithm described in this invention. The principle of this module is to realize the accurate conversion and processing of physical signals into intelligent decisions. Its core data flow is: physical pressure (303 pipeline) → sensing (307 sensor) → signal transmission (304 PCB line) → signal conditioning and digitization (306 circuit board) → intelligent algorithm decision. This process ensures that the raw pressure signal from the front end is acquired, transmitted, and converted with high fidelity and low noise, providing a high-quality and reliable data source for the entire system's decision-making, serving as the "brain" of the entire diagnostic solution. A known standard pressure signal superimposed with white noise can be injected into the input terminal of the signal conditioning circuit board (306) (i.e., the terminal of line 304). The signal-to-noise ratio (SNR) and total harmonic distortion (THD) of the signal can be measured at its output terminal (after ADC sampling) to quantitatively demonstrate that the module's signal conditioning and digitization capabilities effectively improve the quality of the raw signal, laying a solid foundation for accurate diagnosis by subsequent algorithms.

[0067] Optical detection unit (corresponding) Figure 3 Module 3: 308-310): This module is the system's execution and end-point monitoring unit. Its core is the optical sheath flow device (310), where the sheath flow fluid converges with the sample to complete optical detection. A sheath flow pressure sensor (307) is directly and closely installed at the inlet of the optical sheath flow device (310) via a dedicated short conduit (308). The principle of this module is to achieve direct end-point monitoring of critical components. The sensor's close proximity to the optical detection unit allows it to sense the real, unattenuated pressure signal from the most faulty location, ensuring data fidelity from the source. A micro-blockage can be artificially created at the inlet of the optical sheath flow device (310). By comparing the response amplitude and delay time of the pressure sensor in the traditional pump-end installation scheme with the end-point installation scheme of this invention, it can be demonstrated that this invention can detect remote faults earlier and more sensitively.

[0068] Beneficial effects:

[0069] 1. High reliability and low false alarm rate: By integrating the triple diagnostic logic of average value change, mutation value and extreme value, and performing robust processing such as sorting and removing outliers, the diagnostic results are mutually verified, effectively avoiding false actions caused by momentary interference and greatly improving the reliability of the system.

[0070] 2. Possesses forward-looking early warning capabilities, enabling predictive maintenance: It can sensitively capture slow drift or trend changes in pressure, providing early warnings before pressure values ​​exceed limits, giving operators buffer time to intervene, avoiding production interruptions caused by sudden failures, and achieving a leap from "post-event handling" to "pre-event prevention".

[0071] 3. Accurate fault classification and intelligent response strategy: It can accurately distinguish between different types of abnormal states such as transient interference, trend faults and complete hardware failure, and trigger differentiated A-level (immediate shutdown) or B-level (early warning) alarms, so as to minimize unnecessary downtime while ensuring safety.

[0072] 4. Strong anti-interference ability and good environmental adaptability: The robust algorithm adopted can effectively resist pulse interference and measurement outliers, ensuring high reliability of the diagnostic benchmark and enabling the system to maintain extremely high diagnostic accuracy in complex industrial environments.

[0073] 5. Eliminate the risk of tube bursting from the source and ensure thorough safety: By installing the pressure sensor directly and adjacent to the inlet of the optical sheath flow device, combined with the extreme value rapid judgment mechanism, it can sense the real pressure of the key parts most prone to blockage and tube bursting in real time, and immediately cut off the pressure source before dangerous pressure is formed, fundamentally solving the risk that the traditional "direct connection of syringe" solution cannot warn of remote blockage and leakage.

[0074] Good real-time performance and easy integration: The algorithm has low computational load and high efficiency, and can run efficiently on low-cost microcontrollers (MCUs), meeting the stringent real-time requirements of embedded systems. It is easy to integrate and deploy on existing devices.

[0075] The above-disclosed embodiments are merely preferred embodiments of a method and system for diagnosing multiple faults in a sheath flow pressure sensor according to the present invention. They should not be construed as limiting the scope of the present invention. Those skilled in the art can understand that implementing all or part of the above embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for diagnosing multiple faults in a sheath flow pressure sensor, characterized in that, Includes the following steps: Real-time acquisition of pressure signals from the sheath flow system, followed by signal conditioning and analog-to-digital conversion; The sampled data is subjected to equalization filtering to obtain filtered pressure data; Determine whether the current pressure value exceeds the preset absolute safety threshold. If it does, trigger a Level A alarm and immediately shut down the machine. If it does not exceed the threshold, store the data in the buffer and perform detailed analysis after the sample collection is completed. The sample data is quickly sorted, and after removing outliers at both ends, the robust middle segment is extracted. Calculate the abrupt change and the average change of the robust segment. If the abrupt change or the average change exceeds the set threshold, a Class B alarm is triggered. After completing the analysis of the current sample, clear the buffer and proceed to the next sample collection cycle.

2. The method for multiple fault diagnosis of a sheath flow pressure sensor as described in claim 1, characterized in that, The abrupt change is obtained by calculating the mean absolute value of the first-order difference sequence, and is used to detect instantaneous pressure fluctuations. The change in the average value is obtained by comparing the difference between the current average value of the sample segment and the historical average value, and is used to detect pressure trend drift.

3. The method for multiple fault diagnosis of a sheath flow pressure sensor as described in claim 1, characterized in that, The Class A alarm is used to respond to severe pressure exceeding the limit and trigger an immediate shutdown; the Class B alarm is used to respond to trend anomalies and trigger an audible and visual warning.

4. The method for multiple fault diagnosis of a sheath flow pressure sensor as described in claim 1, characterized in that, The equalization filter uses an arithmetic average of three consecutive samples to suppress high-frequency noise.

5. A multiple fault diagnosis system for a sheath flow pressure sensor, used in the multiple fault diagnosis method for the sheath flow pressure sensor according to any one of claims 1-4, characterized in that, It includes a sheath flow pressure sensor, signal conditioning circuit, microcontroller, actuator, and host computer; The sheath flow pressure sensor is used to collect the pressure of the sheath flow system in real time and convert it into an analog electrical signal; The signal conditioning circuit is used to amplify and filter the analog signal; The microcontroller is used to receive the conditioned signal, execute multiple fault diagnosis algorithms, determine the alarm level, and output control commands. The actuator is used to receive control commands and perform corresponding actions, including audible and visual alarms and pump and valve control. The host computer is used to display pressure waveforms, alarm information, and configure diagnostic parameters.

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