Control system of intelligent oil-free linear compressor

Abstract

The invention discloses a control system of an intelligent oil-free linear compressor. The control system is characterized in that the output end of the compressor is connected with a signal port of a sensor; the output end of the sensor signal interface is connected with the signal conditioning circuit; the output end of the signal conditioning circuit is connected with the main control module; the output end of the main control module is respectively connected with the driving circuit, the human-computer interaction interface and the network switching module; the output end of the drive circuit is connected with the compressor and the main control module. The output end of the man-machine interaction interface is connected with the main control module; the output end of the network switching module is respectively connected with the main control module, the network port and the AI co-processing module; the method has real-time working condition recognition, self-adaptive control and fault prediction capabilities, is beneficial to improving the overall performance of the oil-free linear compressor, can further promote the intelligent process of household appliances, and has remarkable technical and industrial values.

Description

Technical Field

[0001] This invention relates to the field of refrigeration equipment technology, and more particularly to a control system for an intelligent oil-free linear compressor. Background Technology

[0002] With the global promotion of energy conservation, emission reduction, and green low-carbon concepts, traditional refrigerator compressor control schemes are no longer sufficient to meet the comprehensive requirements of modern home appliances for high energy efficiency, low noise, and intelligence. In particular, although rotary compressors have a mature structure and low cost, they suffer from problems such as high friction, high operating noise, and slow adjustment response.

[0003] In recent years, oil-free linear compressors have gradually become an important direction for upgrading refrigerator compression systems. These compressors abandon the traditional crankshaft mechanism and adopt an electromagnetically driven piston reciprocating motion structure, thus possessing advantages such as simple structure, high energy efficiency, quiet operation, and long lifespan. However, oil-free linear compressors place higher demands on the response speed and precision of the control system.

[0004] Most current control schemes are based on rule-based start-stop logic, lacking the ability to intelligently identify and adjust strategies according to usage scenarios, resulting in the following prominent problems: 1. Lagging control strategies: Most current systems are based on simple closed-loop control of ambient temperature and refrigerator temperature, unable to sense user behavior, door opening frequency, and changes in load inside the refrigerator, leading to a trade-off between energy saving and comfort; 2. Lagging fault diagnosis: Although linear compressors have a simple mechanical structure, their electromagnetic drive mechanism is extremely sensitive to parameters such as vibration deviation and nonlinear current changes. Initial anomalies are often ignored, but may eventually lead to compressor "cylinder collision" or overheating failure; 3. Lack of local intelligent processing: Although some smart refrigerators have been connected to cloud systems, issues such as data upload delays, cloud load fluctuations, and privacy and security make it difficult to achieve rapid response and high-frequency control; 4. Lack of adaptive learning mechanisms: Traditional control systems cannot learn and optimize themselves according to environmental changes and user habits, leading to model aging and decreased system efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a control system for an intelligent oil-free linear compressor to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention employs the following technical means: A control system for an intelligent oil-free linear compressor, including The compressor's output terminal is connected to the sensor's signal port. The sensor signal interface has its output terminal connected to the signal conditioning circuit. The signal conditioning circuit's output is connected to the main control module. The main control module's output terminals are connected to the drive circuit, the human-machine interface, and the network switching module, respectively. The drive circuit's output terminals are connected to the compressor and the main control module, respectively. The human-computer interaction interface is connected to the main control module at its output end. The network switching module has its output terminals connected to the main control module, the network port, and the AI ​​coprocessing module, respectively.

[0007] In some embodiments, the sensor signal interface includes a temperature signal component, a vibration signal component, a current signal component, a voltage signal component, and a pressure signal component.

[0008] In some embodiments, the temperature signal component includes one ambient temperature signal interface, two cylinder block surface temperature detection interfaces, two winding temperature rise monitoring interfaces, and one infrared temperature sensor (128×128 pixels) detection pipeline (evaporator / condenser / capillary tube) interface; the vibration signal component includes a 4-channel MEMS accelerometer to acquire signals (maximum sampling rate 40KHz) for detecting compressor piston asymmetric vibration, resonance, loosening, and other problems; the current signal component includes one DC controller current monitoring signal and one MOSFET output current monitoring signal; the voltage signal component includes one DC controller voltage monitoring signal and one MOSFET output voltage THD monitoring signal; and the air pressure signal component includes one intake valve pressure monitoring signal and one exhaust valve pressure monitoring signal.

[0009] In some embodiments, the signal conditioning circuit includes analog amplification (adjusting the level to the ADC input range), filtering (RC low-pass + op-amp notch filter), correction (nonlinear compensation), isolation (analog ground and main control system electrical isolation), and impedance matching (improving ADC conversion accuracy).

[0010] In some embodiments, the driving circuitry includes an embedded panel with a touch display.

[0011] In some embodiments, the human-machine interface controls the compressor coil current and voltage changes via MOSFETs and high-frequency SPWM waveforms.

[0012] In some embodiments, the main control module adopts NXP's i.MX RT1170 and RT1180 series.

[0013] In some embodiments, the network switching module is Broadcom's BCM53154.

[0014] In some embodiments, the AI ​​coprocessing module uses Rockchip's RK3588, which integrates an NPU (Neural Processing Unit) and a multi-core ARM CPU, and is compatible with the INT8 quantization model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a local control system with real-time operating condition identification, adaptive control, and fault prediction capabilities. This system not only helps improve the overall performance of oil-free linear compressors but also promotes the intelligentization of home appliances, demonstrating significant technological and industrial value. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the product structure connection according to an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. The following embodiments and drawings are only used to more clearly illustrate the technical solution of this application, and are therefore merely examples and should not be used to limit the scope of protection of this application. The drawings only schematically show the parts related to the technical solution of this application, and do not represent their actual structure as a product.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0019] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0022] In the description of the embodiments of this application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0023] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0024] In this embodiment, a control system for an intelligent oil-free linear compressor includes... The compressor (101) has its output end connected to the sensor signal port (201); The sensor signal interface (201) has its output terminal connected to the signal conditioning circuit (301); The signal conditioning circuit (301) has its output connected to the main control module (401); The main control module (401) has its output terminals connected to the drive circuit (501), the human-machine interface (601), and the network switching module (701), respectively. The drive circuit (501) has its output terminals connected to the compressor (101) and the main control module (401) respectively; The human-computer interaction interface (601) has its output end connected to the main control module (401); The network switching module (701) has its output terminals connected to the main control module (401), the network port (801), and the AI ​​coprocessing module (901), respectively.

[0025] This invention features multi-source signal sensing and acquisition, enabling simultaneous acquisition and processing of various signals such as temperature, vibration, current, voltage, and pressure. Through a local AI co-processing unit, it identifies the compressor's operating status, predicts faults, and analyzes energy consumption. Employing a progressive self-learning strategy, it supports partial model updates and parameter rollback mechanisms to ensure stable system operation. It achieves dynamic modulation of the compressor drive signal to match the optimal control strategy suggested by the AI ​​inference results. A human-machine interface and remote communication interface allow users to easily view the operating status, receive alarm information, and set strategy parameters, supporting OTA upgrades. With an edge inference cycle of <30ms, this invention adapts to rapid dynamic control, supports complex operating condition identification and nonlinear predictive maintenance, and supports cross-validation of multi-dimensional monitoring signals to improve fault detection accuracy. Its modular design allows it to be adapted to various refrigerator product lines.

[0026] In some embodiments, the compressor (101) is used to convert compressed low-temperature, low-pressure refrigerant gas into high-temperature, high-pressure gas to drive a refrigeration cycle and achieve cooling inside the refrigerator. In some embodiments, the sensor signal interface (201) includes a temperature signal component, a vibration signal component, a current signal component, a voltage signal component, and a pressure signal component, for monitoring the operating status of the compressor.

[0027] In some embodiments, the temperature signal component includes one ambient temperature signal interface, two cylinder block surface temperature detection interfaces, two winding temperature rise monitoring interfaces, and one infrared temperature sensor (128×128 pixels) detection pipeline (evaporator / condenser / capillary tube) interface; the vibration signal component includes a 4-channel MEMS accelerometer to acquire signals (maximum sampling rate 40KHz) for detecting compressor piston asymmetric vibration, resonance, loosening, and other problems; the current signal component includes one DC controller current monitoring signal and one MOSFET output current monitoring signal; the voltage signal component includes one DC controller voltage monitoring signal and one MOSFET output voltage THD monitoring signal; and the air pressure signal component includes one intake valve pressure monitoring signal and one exhaust valve pressure monitoring signal.

[0028] In some embodiments, the signal conditioning circuit (301) includes analog amplification (adjusting the level to the ADC input range), filtering (RC low-pass + op-amp notch filter), correction (nonlinear compensation), isolation (analog ground and main control system electrical isolation), and impedance matching (improving ADC conversion accuracy), and uses hardware circuitry to preprocess the signals acquired by the aforementioned sensor signal interface (201).

[0029] In some embodiments, the main control module (401) is used to perform algorithmic processing on the signals received by the sensor from the compressor (101); to send and receive data with the human-machine interface (601); to output frequency signals to the drive circuit (501); and to receive current and voltage information fed back by the drive circuit (501). In some embodiments, the main control module (401) adopts NXP's i.MX RT1170 and RT1180 series. It features high clock frequency, high concurrency, and low power consumption, and supports dual-core collaborative operation: one core is used for real-time control, and the other core is used for edge preprocessing, quantization, and data management. Its main functions include: generating and modulating PWM drive signals (SPWM, frequency 16~30kHz, variable duty cycle); receiving all sensor data and timestamping it; implementing sliding window filtering and vibration signal FFT transformation (maximum 10K points); constructing feature vectors (including state trends, short-term spectrum, control parameters, etc.) and performing quantization (int8); transmitting feature vectors to the AI ​​co-processing module through the network exchange module; receiving inference results and combining them with traditional control strategies to adjust the final control strategy in a timely manner; displaying the status of the management interface and receiving user commands; and having local storage capabilities to record historical fault tags and model feedback logs, enabling data traceability and operating condition learning.

[0030] In some embodiments, the drive circuit (501) includes an embedded panel with a touch display for driving the compressor (101) to operate normally.

[0031] In some embodiments, the human-machine interface (601) controls the change of compressor coil current and voltage through MOSFET and high-frequency SPWM waveform to display the current operating status and fault monitoring status of the machine to the user; and to input the user's update operation information to the main control module (401), including but not limited to working mode, control strategy, etc.

[0032] In some embodiments, the network switching module (701) uses Broadcom's BCM53154 to forward data. For example, it receives the processed data from the main control module (401) and outputs it to the AI ​​coprocessing module (901); it receives the processing results from the AI ​​coprocessing module (901) and outputs them to the main control module (401), etc.

[0033] In some embodiments, the network port (801) is used to forward data or debug functions of the main control module (401) and the AI ​​coprocessing module (901) through the network switching module (701).

[0034] In some embodiments, the AI ​​coprocessing module (901) uses Rockchip's RK3588, integrating an NPU (Neural Processing Unit) and a multi-core ARM CPU, and is adapted to the INT8 quantization model. It receives data processed by the main control module (401), completes the inference process based on the trained and locally deployed model structure, and returns the inference result to the main control module (401). The integration of an NPU and a multi-core ARM CPU, and adaptation to the INT8 quantization model, offers advantages in high performance and low latency. The model deployed in this system is a cascaded structure of 1D-CNN + LSTM. 1D-CNN is used to extract local features within a short time window, while LSTM is used to capture the temporal dependencies between long-term operating trends and multi-dimensional signals. The model training adopts a supervised learning approach. The dataset comes from laboratory simulation tests and field operating condition collection, and includes the following three types: fault labels: air leakage, cylinder collision, voltage fluctuation, etc.; operating condition labels: cold start, high load, frequent start-stop, frequent door opening, etc.; energy consumption labels: cold load matching efficiency index, energy saving ratio. The model outputs the following three results for each inference: current operating condition: multi-class output; fault prediction confidence: whether it has entered a pre-fault state based on sliding window trend prediction; recommended drive control strategy update suggestions: such as adjusting duty cycle, reducing frequency, and increasing half-wave points.

[0035] The AI ​​intelligent strategy fusion and dynamic update are mainly reflected in the following: After receiving feedback from the AI ​​coprocessing module, the main control module will process the control signal in the following fusion method: Initial feedback: The original control command and the AI ​​recommended scheme are fused at a ratio of 9:1; If the system runs stably for 24 hours: The fusion ratio is gradually increased, +1% each time, up to a maximum of 5:5; If the system malfunctions or deviates from the normal value, it will roll back to the last stable configuration.

[0036] The self-learning mechanism is mainly manifested in the following way: the AI ​​module adopts a "freeze-fine-tuning" mechanism for incremental learning. Freeze the backbone model (the main structures of CNN and LSTM remain unchanged); update the connection weights using sampled data from the past 24 hours of stable operation; enter the validation cycle after each learning cycle, and immediately roll back if performance degrades; the learning cycle is triggered daily or after a failure event to ensure that the model continuously adapts to the current actual state as the equipment ages and the environment changes; this strategy ensures that the system gradually converges to better control parameters without completely relying on the initial AI model.

[0037] The specific processing procedure of this invention during use is as follows: When the device starts, the main control module (401) actively reads the backup of the instruction sent from the human-machine interface (601) to the main control module (401) last time, and the main control module (401) outputs an SPWM waveform with an adjustable duty cycle (amplitude) of 16KHz~30KHz to the drive module (501). Finally, the drive module drives the compressor to start working.

[0038] During operation, the compressor connects a total of 16 sets of signals to the sensor signal interface (201), including one set of infrared temperature sensing signals, which are sent to the main control module via the signal conditioning circuit (301). The main control module performs software processing on all sampled signals, including filtering, weighted averaging, int8 quantization, and timestamp synchronization, to generate feature vectors required for AI edge processing. In particular, it performs a maximum 10K-point FFT on the collected vibration signals to generate their spectral signals.

[0039] All feature vector data are labeled with feature categories, including electrical features (current / voltage, etc.), mechanical features (time-frequency response curve of vibration), thermal features (ambient temperature, equipment temperature), control signal features (target cooling temperature, output duty cycle, carrier frequency, number of half-wave points), and state trend features (running time, cycle time, idle time, load curve, historical fault labels). The data is then sent to the AI ​​co-processing module (901) through the network exchange module (701).

[0040] The labeled feature data is inferred in the AI ​​coprocessing module (901) to output three objectives: operating condition identification, fault monitoring and prediction, and energy consumption optimization. The inference output is returned to the main control module (401) through the network exchange module (701), and some information is displayed on the human-computer interaction interface (601).

[0041] The specific embodiments disclosed in this invention fall within the scope of protection of the claims of this invention, and are specific subordinate implementations of the characteristic parts of this invention. The protection content of the specific embodiments is merely an explanation of the scope of protection of the claims of this invention, and the scope of protection of this invention is not limited to the protection content of the specific embodiments. The protection content of the specific embodiments should not be construed as a limitation on the scope of protection of the claims of this invention. All product structural connection relationships falling within the scope of protection of this invention are also within the scope of protection of this invention. Conventional technical improvements to the structure of product components without departing from the essence of protection of this invention, such as the improvements to the structure of some parts of the product as described in the specific embodiments of this invention, will also fall within the essence of protection of this invention.

Claims

1. A control system for an intelligent oil-free linear compressor, characterized in that: include The compressor (101) has its output end connected to the sensor signal port (201); The sensor signal interface (201) has its output terminal connected to the signal conditioning circuit (301); The signal conditioning circuit (301) has its output connected to the main control module (401); The main control module (401) has its output terminals connected to the drive circuit (501), the human-machine interface (601), and the network switching module (701), respectively. The drive circuit (501) has its output terminals connected to the compressor (101) and the main control module (401) respectively; The human-computer interaction interface (601) has its output end connected to the main control module (401); The network switching module (701) has its output terminals connected to the main control module (401), the network port (801), and the AI ​​coprocessing module (901), respectively.

2. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The sensor signal interface (201) includes a temperature signal component, a vibration signal component, a current signal component, a voltage signal component, and a pressure signal component.

3. The control system for an intelligent oil-free linear compressor according to claim 3, characterized in that: The temperature signal component includes one ambient temperature signal interface, two cylinder block surface temperature detection interfaces, two winding temperature rise monitoring interfaces, one infrared temperature sensor, and a detection pipeline interface; the vibration signal component includes a 4-channel MEMS accelerometer to collect signals for detecting compressor piston asymmetric vibration, resonance, and loosening issues; the current signal component includes one DC controller current monitoring signal and one MOSFET output current monitoring signal; the voltage signal component includes one DC controller voltage monitoring signal and one MOSFET output voltage THD monitoring signal; the air pressure signal component includes one intake valve pressure monitoring signal and one exhaust valve pressure monitoring signal.

4. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The signal conditioning circuit (301) is used for analog amplification, filtering, correction, isolation, and impedance matching.

5. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The drive circuit (501) includes an embedded panel with a touch display.

6. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The human-machine interface (601) controls the change of compressor coil current and voltage through MOSFET and high-frequency SPWM waveform.

7. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The main control module (401) adopts NXP's i.MX RT1170 and RT1180 series.

8. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The network switching module (701) is Broadcom's BCM53154.

9. The control system for an intelligent oil-free linear compressor according to claim 1, characterized in that: The AI ​​coprocessor module (901) uses Rockchip's RK3588 to integrate an NPU and a multi-core ARM CPU to adapt to the INT8 quantization model.

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