Air conditioner outdoor unit fault monitoring system and method based on vibration sensor

Abstract

The embodiment of the invention discloses an air conditioner outdoor unit fault monitoring system and method based on a vibration sensor. According to one specific embodiment, the air conditioner outdoor unit fault monitoring system comprises an acceleration sensor, a signal collecting device, a signal processing chip, a signal analyzing chip and an alarm device, and the acceleration sensor is configured to convert acceleration of a compressor pipeline into a vibration electric signal; the signal acquisition equipment is configured to acquire the vibration electric signal converted by the acceleration sensor and cache the vibration electric signal into the cache chip; the signal processing chip is configured to pre-process the vibration electric signals and extract signal features of the pre-processed vibration electric signals; the signal analysis chip is configured to execute a signal analysis task; the alarm device is configured to give an alarm to an associated user and execute a preset fault emergency operation. According to the embodiment, the situation that the air conditioning system cannot be used normally is avoided, and the maintenance cost of the air conditioning system is reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a failure monitoring system and a failure monitoring method for an outdoor unit of an air conditioner based on a vibration sensor. Background Art

[0002] As a core component in an existing air conditioner system, the operation state of a compressor has a great impact on the system performance. How to determine whether the compressor has a failure has become an important research topic. Currently, when determining whether the compressor has a failure, the commonly used method is to determine whether the compressor has a failure by determining changes in physical quantities such as the temperature, current, and voltage of the compressor.

[0003] However, when using the above method to determine whether the compressor has a failure, there are often the following technical problems:

[0004] When determining whether the compressor has a failure by determining changes in physical quantities such as the temperature, current, and voltage of the compressor, the sensitivity is low and the response is lagging, making it difficult to detect early hidden dangers in a timely manner, resulting in the air conditioner system being unable to be used normally. In addition, when detecting changes in physical quantities, the manual maintenance cost is high and the detection accuracy is difficult to guarantee.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0006] This content part of the present disclosure is used to introduce the inventive concept in a brief form, and these inventive concepts will be described in detail in the following detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose a failure monitoring system and a failure monitoring method for an outdoor unit of an air conditioner based on a vibration sensor to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide a failure monitoring system for an outdoor unit of an air conditioner based on a vibration sensor. The failure monitoring system for the outdoor unit of the air conditioner includes: an acceleration sensor, a signal acquisition device, a signal processing chip, a signal analysis chip, and an alarm device. Among them, the acceleration sensor is configured to convert the acceleration of the compressor pipeline into a vibration electrical signal; the signal acquisition device is configured to acquire the vibration electrical signal converted by the acceleration sensor and cache the vibration electrical signal into a cache chip; the signal processing chip is configured to preprocess the vibration electrical signal and extract the signal features of the preprocessed vibration electrical signal; the signal analysis chip is configured to perform a signal analysis task based on the signal features and output an analysis result; the alarm device is configured to alarm the associated user based on the analysis result and perform a preset failure emergency operation.

[0009] Optionally, the acceleration sensor is a MEMS triaxial acceleration sensor.

[0010] Optionally, the acceleration sensor is installed on the surface of the compressor pipeline.

[0011] In a second aspect, some embodiments of the present disclosure provide a failure monitoring method, which includes: real-time collecting the vibration electrical signal of a compressor included in an outdoor unit of an air conditioner, where the vibration electrical signal is a vibration acceleration signal; performing a decomposition process on the vibration acceleration signal to generate an energy entropy change feature set; performing a fast Fourier transform process on the vibration acceleration signal to generate a frequency domain signal; generating a peak information set and a weighted root mean square value based on the frequency domain signal; combining the energy entropy change feature set, the peak information set, and the weighted root mean square value into a composite feature vector; performing a feature analysis on the composite feature vector to generate a feature analysis result; generating a failure alarm information based on the feature analysis result, and performing a preset failure emergency operation corresponding to the feature analysis result.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device on which one or more programs are stored. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the second aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the method described in any implementation manner of the second aspect is implemented.

[0014] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the air conditioner outdoor unit fault monitoring system based on vibration sensors in some embodiments of the present disclosure, the situation where the air conditioner system cannot be used normally is avoided, and the maintenance cost of the air conditioner system is reduced. Specifically, the reasons for the abnormal use of the air conditioner system and the high maintenance cost of the air conditioner system are as follows: When determining whether the compressor fails by judging the changes in physical quantities such as the temperature, current, and voltage of the compressor, the sensitivity is low, the response is lagging, and it is difficult to detect early hidden dangers in a timely manner, resulting in the abnormal use of the air conditioner system. In addition, when detecting changes in physical quantities, the manual maintenance cost is high, and the detection accuracy is difficult to guarantee. Based on this, in some embodiments of the air conditioner outdoor unit fault monitoring system based on vibration sensors of the present disclosure, the above-mentioned air conditioner outdoor unit fault monitoring system includes: an acceleration sensor, a signal acquisition device, a signal processing chip, a signal analysis chip, and an alarm device. Among them, the above-mentioned acceleration sensor is configured to convert the acceleration of the compressor pipeline into a vibration electrical signal; the above-mentioned signal acquisition device is configured to collect the vibration electrical signal converted by the above-mentioned acceleration sensor and cache the vibration electrical signal into the cache chip; the above-mentioned signal processing chip is configured to preprocess the above-mentioned vibration electrical signal and extract the signal characteristics of the preprocessed vibration electrical signal; the above-mentioned signal analysis chip is configured to perform a signal analysis task based on the above-mentioned signal characteristics and output an analysis result; the above-mentioned alarm device is configured to alarm the associated user based on the above-mentioned analysis result and perform a preset fault emergency operation. Thus, an acceleration sensor can be arranged on the compressor pipeline of the air conditioner outdoor unit, and by extracting and analyzing the characteristics of the obtained vibration acceleration electrical signal, it can be determined whether the compressor fails, and corresponding fault emergency operations can be performed, so that compressor faults can be discovered in a timely manner, and the compressor can be repaired in a timely manner, avoiding the situation where the air conditioner system cannot be used normally. Also, because of performing fault emergency operations on the compressor, it is possible to avoid the entire air conditioner system from failing due to compressor faults, thereby reducing the maintenance cost of the air conditioner system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0016] Figure 1 is a schematic structural diagram of some embodiments of the air conditioner outdoor unit fault monitoring system based on vibration sensors according to the present disclosure;

[0017] Figure 2 is a flowchart of some embodiments of the fault monitoring method according to the present disclosure;

[0018] Figure 3 It is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed implementation manners

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0020] In addition, it should be noted that for ease of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0021] It should be noted that concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0022] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0023] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0024] The present disclosure will be described in detail below with reference to the drawings and in combination with embodiments.

[0025] Figure 1 A schematic structural diagram of some embodiments of an air conditioner outdoor unit fault monitoring system based on a vibration sensor according to the present disclosure is shown. The air conditioner outdoor unit fault monitoring system based on a vibration sensor includes: an acceleration sensor 102, a signal acquisition device 103, a signal processing chip 104, a signal analysis chip 105, and an alarm device 106, where,

[0026] In some embodiments, the acceleration sensor 102 is configured to convert the acceleration of the compressor pipeline 101 into a vibration electrical signal. Among them, the acceleration sensor 102 may be a sensor for collecting the magnitude of the force generated by the vibration of the compressor pipeline 101. The signal acquisition device 103 is configured to collect the vibration electrical signal converted by the acceleration sensor 102 and cache the vibration electrical signal in the cache chip. Among them, the cache chip may be a CPU cache. The signal processing chip 104 is configured to perform preprocessing on the vibration electrical signal and extract the signal features of the preprocessed vibration electrical signal. The preprocessing may be Fourier transform processing. The signal analysis chip 105 is configured to perform a signal analysis task based on the signal features and output an analysis result. The alarm device 106 is configured to alarm the associated user based on the analysis result and perform a preset fault emergency operation.

[0027] Optionally, the acceleration sensor 102 is a MEMS triaxial acceleration sensor (microelectromechanical system sensor).

[0028] Optionally, the acceleration sensor 102 is installed on the surface of the compressor pipeline 101.

[0029] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the air conditioner outdoor unit fault monitoring system based on vibration sensors in some embodiments of the present disclosure, the situation where the air conditioner system cannot be used normally is avoided, and the maintenance cost of the air conditioner system is reduced. Specifically, the reasons for the abnormal use of the air conditioner system and the high maintenance cost of the air conditioner system are as follows: When determining whether the compressor fails by judging the changes in physical quantities such as the temperature, current, and voltage of the compressor, the sensitivity is low, the response is lagging, and it is difficult to detect early hidden dangers in a timely manner, resulting in the inability to use the air conditioner system normally. In addition, when detecting changes in physical quantities, the manual maintenance cost is high, and the detection accuracy is difficult to guarantee. Based on this, in some embodiments of the present disclosure, the air conditioner outdoor unit fault monitoring system based on vibration sensors includes: an acceleration sensor, a signal acquisition device, a signal processing chip, a signal analysis chip, and an alarm device. Among them, the acceleration sensor is configured to convert the acceleration of the compressor pipeline into a vibration electrical signal; the signal acquisition device is configured to collect the vibration electrical signal converted by the acceleration sensor and cache the vibration electrical signal into a cache chip; the signal processing chip is configured to preprocess the vibration electrical signal and extract the signal characteristics of the preprocessed vibration electrical signal; the signal analysis chip is configured to perform a signal analysis task based on the signal characteristics and output an analysis result; the alarm device is configured to alarm the associated user based on the analysis result and perform a preset fault emergency operation. Thus, an acceleration sensor can be arranged on the compressor pipeline of the air conditioner outdoor unit, and by extracting and analyzing the characteristics of the obtained vibration acceleration electrical signal, it is possible to determine whether the compressor fails and perform corresponding fault emergency operations, so that compressor faults can be detected in a timely manner, and the compressor can be repaired in a timely manner, avoiding the situation where the air conditioner system cannot be used normally. Also, because of the fault emergency operation on the compressor, it is possible to avoid the failure of the entire air conditioner system caused by the compressor fault, thereby reducing the maintenance cost of the air conditioner system.

[0030] Continuing to refer to Figure 2 , a flowchart 200 of some embodiments of a fault monitoring method applicable to an air conditioner outdoor unit fault monitoring system based on vibration sensors according to the present disclosure is shown. This fault monitoring method is applied to an air conditioner outdoor unit fault monitoring system as shown in Figure 1 and includes the following steps:

[0031] Step 201, collect in real time the vibration electrical signal of the compressor included in the air conditioner outdoor unit.

[0032] In some embodiments, the execution subject of the fault monitoring method (such as a server) can collect the vibration electrical signals of the compressor included in the outdoor unit of the air conditioner in real time. Among them, the above vibration electrical signals are vibration acceleration signals. Here, the vibration acceleration signals emitted by the acceleration sensor can be collected through the signal acquisition device included in the above outdoor unit fault monitoring system.

[0033] Step 202, decompose the above vibration acceleration signals to generate an energy entropy change feature set.

[0034] In some embodiments, the above execution subject can decompose the above vibration acceleration signals to generate an energy entropy change feature set.

[0035] In the process of adopting technical solutions to solve the above technical problems, the following technical problems often accompany: when performing feature analysis through vibration electrical signals, nonlinear vibration features often cannot be identified, resulting in the analysis results not matching the actual results, thus making it impossible to determine whether the compressor is faulty. Considering the above technical problems and combining the existing technical status, the following solutions can be decided to be adopted.

[0036] In practice, an energy entropy change feature set can be generated through the following steps:

[0037] The first step is to perform a first decomposition process on the above vibration acceleration signals based on a preset basis function to generate a low-frequency signal and a high-frequency signal. In practice, Daubechies 4th-order wavelet (db4) can be used as the basis function. Here, the low-frequency signal and the high-frequency signal of the vibration acceleration signals can be extracted through a low-pass filter and a high-pass filter respectively.

[0038] The second step is to perform the following decomposition steps based on the above low-frequency signal and the above high-frequency signal:

[0039] The first decomposition step is to decompose the above low-frequency signal and the above high-frequency signal respectively to generate at least one low-frequency signal and at least one high-frequency signal. Here, the at least one low-frequency signal and the at least one high-frequency signal decomposed in the previous layer are subjected to secondary decomposition through a low-pass filter and a high-pass filter to generate multiple low-frequency signals and multiple high-frequency signals.

[0040] The second decomposition step is to determine the number of times the above decomposition step has been executed.

[0041] The third decomposition step is to, in response to the above number of executed times being equal to the preset number of executed times, generate a decomposition signal tree based on the generated low-frequency signals and high-frequency signals. Here, the above preset number of executed times can be the number of times of executing the decomposition step set in advance. As an example, the above preset number of executed times can be 4 times. The above decomposition signal tree can be a tree-like structure with a five-layer structure and 32 nodes.

[0042] In the third step, determine the energy values of the nodes in the last layer of the above-mentioned decomposed signal tree to obtain a set of node energy values. In practice, the energy values of the nodes in the last layer of the above-mentioned decomposed signal tree can be determined by the following formula:

[0043]

[0044] where C i，k represents the coefficient of the i-th node at the k-th time point. N represents the coefficient length.

[0045] In the fourth step, normalize each node energy value in the above-mentioned set of node energy values to generate a normalized energy value, obtain a set of normalized energy values, and determine the normalized energy entropy values of each normalized energy value in the above-mentioned set of normalized energy values to obtain a set of normalized energy entropy values. In practice, the normalized energy value and the normalized energy entropy value can be determined by the following formula:

[0046]

[0047] where p i，m represents the energy proportion of the m-th sub-segment in the i-th node (the node coefficients are divided into M segments, and the energy of each segment is normalized).

[0048] In the fifth step, determine the entropy change rate corresponding to each normalized energy entropy value in the above-mentioned set of normalized energy entropy values, and based on the determined entropy change rates, select a preset number of target nodes to obtain a set of target nodes. In practice, for each of the above nodes, sort each node in descending order according to the corresponding entropy change rate to obtain a node sequence, and select a preset number of nodes from the above node sequence as target nodes.

[0049] In the sixth step, perform feature extraction processing on each target node in the above-mentioned set of target nodes to generate energy entropy change features, and obtain a set of energy entropy change features. Here, the above-mentioned feature extraction processing may be to extract the center frequency, energy proportion, and entropy change gradient of each target node. In practice, the center frequency and energy proportion can be determined by the following formula:

[0050]

[0051] where f c represents the center frequency, and l represents the l-th layer of the decomposed signal tree.

[0052] The above first step - sixth step and their related content are an inventive point of the embodiments of the present disclosure. In combination with the following step "step 207", it solves the technical problem that "when performing feature analysis through vibration electrical signals, non - linear vibration features are often unable to be recognized, resulting in the analysis result not matching the actual result, thereby making it impossible to determine whether the compressor is faulty". The factors that lead to the inability to determine whether the compressor is faulty are often as follows: when performing feature analysis through vibration electrical signals, non - linear vibration features are often unable to be recognized, resulting in the analysis result not matching the actual result, thereby making it impossible to determine whether the compressor is faulty. If the above - mentioned factors are solved, the effect of accurately determining whether the compressor is faulty can be achieved. To achieve this effect, first, based on a preset basis function, perform a first decomposition process on the above - mentioned vibration acceleration signal to generate a low - frequency signal and a high - frequency signal. Thus, wavelet packet decomposition can be performed through the basis function. Second, based on the above - mentioned low - frequency signal and the above - mentioned high - frequency signal, perform the following decomposition steps: respectively perform decomposition processing on the above - mentioned low - frequency signal and the above - mentioned high - frequency signal to generate at least one low - frequency signal and at least one high - frequency signal; determine the number of times the above - mentioned decomposition step has been executed; in response to the number of times the above - mentioned decomposition step has been executed being equal to the preset number of executions, generate a decomposition signal tree based on the generated low - frequency signals and high - frequency signals; determine the energy values of each node in the last layer of the above - mentioned decomposition signal tree to obtain a set of node energy values. Thus, the energy value corresponding to each node can be determined. Third, perform normalization processing on each node energy value in the above - mentioned set of node energy values to generate a normalized energy value, obtain a set of normalized energy values, and determine the normalized energy entropy values of each normalized energy value in the above - mentioned set of normalized energy values to obtain a set of normalized energy entropy values. Thus, the energy entropy values of each node can be determined through normalization. Fourth, determine the entropy change rate corresponding to each normalized energy entropy value in the above - mentioned set of normalized energy entropy values, and based on the determined entropy change rates, select a preset number of target nodes to obtain a set of target nodes. Thus, sensitive nodes can be screened out. Fifth, perform feature extraction processing on each target node in the above - mentioned set of target nodes to generate an energy entropy change feature, obtain a set of energy entropy change features. In combination with the following step "step 207", generate a fault warning message based on the above - mentioned feature analysis result, and perform a preset fault emergency operation corresponding to the above - mentioned feature analysis result. Thus, non - linear vibration features can be revealed through wavelet packet energy entropy, thereby avoiding the situation where the analysis result does not match the actual result due to the inability to recognize non - linear vibration features, resulting in the inability to determine whether the compressor is faulty.

[0053] Step 203: Perform a fast Fourier transform process on the above - mentioned vibration acceleration signal to generate a frequency - domain signal.

[0054] In some embodiments, the above - mentioned execution subject can perform a fast Fourier transform process on the above - mentioned vibration acceleration signal to generate a frequency - domain signal.

[0055] In practice, the fast Fourier transform process can be carried out through the following steps:

[0056] In the first step, based on a preset duration, a vibration acceleration signal segment is determined. Among them, the above preset duration can be the duration of the collected signal set in advance. As an example, the above preset duration can be 10s. Here, the first 10s of the vibration acceleration signal can be intercepted to generate a vibration acceleration signal segment.

[0057] In the second step, the fast Fourier transform is performed on the above vibration acceleration signal segment to generate a transformed frequency-domain signal as the frequency-domain signal.

[0058] Step 204, based on the above frequency-domain signal, a peak information set and a weighted root mean square value are generated.

[0059] In some embodiments, the above execution entity can generate a peak information set and a weighted root mean square value based on the above frequency-domain signal.

[0060] In some optional implementation manners of some embodiments, the above execution entity can generate a peak information set through the following steps:

[0061] In the first step, each peak in the spectrum corresponding to the above frequency-domain signal is sorted to generate a peak sequence. Among them, the above sorting can be in descending order according to the peak height

[0062] In the second step, a preset number of peaks are selected from the above peak sequence as target peaks to obtain a target peak set, and the peak information corresponding to each target peak in the above target peak set is determined to obtain a peak information set. Among them, the peak information in the above peak information set includes frequency, amplitude, and phase. The above preset number can be the number of selected peaks set in advance. For example, the above preset number can be 10.

[0063] Optionally, after the third step, the following steps are further included:

[0064] In the third step, the signal length of the above frequency-domain signal and each node corresponding to the above signal length are determined.

[0065] In the fourth step, at least one time-domain acceleration component corresponding to each of the above nodes is determined to obtain a time-domain acceleration component set, and a time-domain acceleration sequence corresponding to the above time-domain acceleration component set is determined. In practice, the time-domain acceleration can be determined through the following formula:

[0066]

[0067] Among them, a represents the time-domain acceleration, a x 、ay and a z respectively represent the components of the time-domain acceleration on the x, y, and z axes, serving as the time-domain acceleration components.

[0068] Step 5: Based on the above total acceleration and the above signal length, perform a discrete Fourier transform on the time-domain acceleration sequence to generate frequency-domain acceleration data. Here, the above time-domain acceleration sequence can be expressed as a[n] (n = 0, 1, 2... N - 1). The frequency-domain acceleration data can be generated through the following formula:

[0069]

[0070] where A[k] represents the frequency-domain value at frequency index k, and j represents the imaginary unit.

[0071] Step 6: Based on the above frequency-domain acceleration data, determine the power spectral density corresponding to the above time-domain acceleration sequence. Here, the above power spectral density can be determined through the following formula:

[0072]

[0073] where f s represents the sampling frequency. The k-th frequency component f k is:

[0074] Step 7: Based on the frequency weighting function, perform frequency weighting on the above power spectral density to generate a weighted density. In practice, the above frequency weighting function can be expressed through the following formula:

[0075]

[0076] where f c represents the cut-off frequency. Here, f c is set to 100 Hz. The weighted density can be expressed as: WS aa (f k ) = S aa (f k ) · W(f k ).

[0077] Step 8: Based on the above weighted density, generate the weighted root mean square value corresponding to the above frequency-domain signal. In practice, the above weighted root mean square value can be expressed by the following formula:

[0078]

[0079] Step 205: Combine the above energy entropy change feature set, the above peak information set, and the above weighted root mean square value into a composite feature vector.

[0080] In some embodiments, the above-mentioned execution entity may combine the above-mentioned energy entropy change feature set, the above-mentioned peak information set, and the above-mentioned weighted root mean square value into a composite feature vector.

[0081] Step 206: Perform feature analysis on the above-mentioned composite feature vector to generate a feature analysis result.

[0082] In some embodiments, the above-mentioned execution entity may perform feature analysis on the above-mentioned composite feature vector to generate a feature analysis result. In practice, the above-mentioned composite feature vector may be input into a pre-trained feature analysis model to obtain a feature analysis result. Among them, the above-mentioned feature analysis model may be a neural network model constructed with Python + TensorFlow. The above-mentioned feature analysis model includes three layers: the first layer, the input layer, is used to extract the vector features of the composite feature vector. The second layer, the activation layer, includes two hidden layers, with 64 neurons in each layer, and is analyzed through the activation function ReLU. The third layer, the output layer, includes a softmax layer or a sigmoid layer. Among them, the softmax layer is used for multi-classification. The sigmoid layer is used for binary classification. The loss function of the above-mentioned feature analysis model is the cross-entropy loss function, and the optimizer used is Adam.

[0083] Step 207: Generate a fault warning message based on the above-mentioned feature analysis result, and execute a preset fault emergency operation corresponding to the above-mentioned feature analysis result.

[0084] In some embodiments, the above-mentioned execution entity may generate a fault warning message based on the above-mentioned feature analysis result, and execute a preset fault emergency operation corresponding to the above-mentioned feature analysis result. Among them, the above-mentioned fault warning message may be information used to indicate that a compressor has failed. The above-mentioned preset fault emergency operation may be an emergency operation preset when the compressor fails. As an example, the above-mentioned preset fault emergency operation may be to control the air conditioning system to shut down and operate.

[0085] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the fault monitoring method of some embodiments of the present disclosure, the situation where the air-conditioning system cannot be used normally is avoided, and the maintenance cost of the air-conditioning system is also reduced. Specifically, the reasons for the abnormal use of the air-conditioning system and the high maintenance cost of the air-conditioning system are as follows: When determining whether the compressor fails by judging the changes in physical quantities such as the temperature, current, and voltage of the compressor, the sensitivity is low, the response is lagged, and it is difficult to detect early hidden dangers in time, resulting in the abnormal use of the air-conditioning system. In addition, when detecting the changes in physical quantities, the manual maintenance cost is high, and the detection accuracy is difficult to guarantee. Based on this, the fault monitoring method of some embodiments of the present disclosure, first, collects the vibration electrical signals of the compressor included in the outdoor unit of the air conditioner in real time. Thus, the vibration electrical signals of the compressor pipeline can be collected. Second, decompose and process the above vibration acceleration signals to generate an energy entropy change feature set. Thus, the energy entropy change characteristics represented by the vibration signals can be determined. Third, perform fast Fourier transform processing on the above vibration acceleration signals to generate frequency-domain signals; based on the above frequency-domain signals, generate a peak information set and a weighted root mean square value. Thus, each peak information and the corresponding weighted root mean square value in the signal spectrum can be determined. Fourth, combine the above energy entropy change feature set, the above peak information set, and the above weighted root mean square value into a composite feature vector. Thus, a composite feature for analyzing the signal can be obtained. Fifth, perform feature analysis on the above composite feature vector to generate a feature analysis result; based on the above feature analysis result, generate a fault warning message and execute a preset fault emergency operation corresponding to the above feature analysis result. Thus, it can be determined whether the compressor fails through the analysis result, and an emergency operation can be executed when a failure occurs to avoid further damage to the air-conditioning system. Therefore, the situation where the air-conditioning system cannot be used normally can be avoided, and it can be determined whether the compressor fails only through the vibration electrical signals collected by the sensor, reducing the maintenance cost of the air-conditioning system.

[0086] Reference is made below to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage ranges of the embodiments of the present disclosure.

[0087] As Figure 3As shown, the electronic device 300 may include a processing device 301 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 302 or a program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0088] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wirelesly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be implemented or had alternatively. Figure 3 Each block shown in may represent one device or, as needed, multiple devices.

[0089] Specifically, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above functions defined in the methods of some embodiments of the present disclosure are executed.

[0090] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0091] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0092] The above computer-readable medium may be included in the above electronic device; or it may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device is caused to: collect in real time the vibration electrical signal of the compressor included in the outdoor unit of the air conditioner, where the above vibration electrical signal is a vibration acceleration signal; perform decomposition processing on the above vibration acceleration signal to generate an energy entropy change feature set; perform fast Fourier transform processing on the above vibration acceleration signal to generate a frequency domain signal; generate a peak information set and a weighted root mean square value based on the above frequency domain signal; combine the above energy entropy change feature set, the above peak information set and the above weighted root mean square value into a composite feature vector; perform feature analysis on the above composite feature vector to generate a feature analysis result; generate a fault warning information based on the above feature analysis result, and perform a preset fault emergency operation corresponding to the above feature analysis result.

[0093] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0095] The functions described above can be performed, at least in part, by one or more hardware logic components. By way of example, and without limitation, the types of hardware logic components that may be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0096] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An air conditioner outdoor unit fault monitoring system based on vibration sensors, the air conditioner outdoor unit fault monitoring system comprising: An acceleration sensor, a signal acquisition device, a signal processing chip, a signal analysis chip, and an alarm device, wherein, The acceleration sensor is configured to convert the acceleration of the compressor pipeline into a vibration electrical signal; The signal acquisition device is configured to acquire the vibration electrical signal converted by the acceleration sensor and cache the vibration electrical signal into a cache chip; The signal processing chip is configured to preprocess the vibration electrical signal and extract the signal features of the preprocessed vibration electrical signal; The signal analysis chip is configured to perform a signal analysis task based on the signal features and output an analysis result; The alarm device is configured to alarm the associated user based on the analysis result and perform a preset fault emergency operation.

2. The air conditioner outdoor unit fault monitoring system based on a vibration sensor according to claim 1, wherein, The acceleration sensor is a MEMS triaxial acceleration sensor.

3. The air conditioner outdoor unit fault monitoring system based on a vibration sensor according to claim 1, wherein, The acceleration sensor is installed on the surface of the compressor pipeline.

4. A fault monitoring method applicable to the fault monitoring system of an air conditioner outdoor unit based on a vibration sensor as described in claims 1-4, the fault monitoring method includes: Real-time acquisition of the vibration electrical signal of the compressor included in the air conditioner outdoor unit, wherein the vibration electrical signal is a vibration acceleration signal; Decompose and process the vibration acceleration signal to generate an energy entropy change feature set; Perform a fast Fourier transform process on the vibration acceleration signal to generate a frequency domain signal; Based on the frequency domain signal, generate a peak information set and a weighted root mean square value; Combine the energy entropy change feature set, the peak information set, and the weighted root mean square value into a composite feature vector; Perform feature analysis on the composite feature vector to generate a feature analysis result; Based on the feature analysis result, generate a fault alarm message and perform a preset fault emergency operation corresponding to the feature analysis result.

5. The fault monitoring method according to claim 4, wherein, The performing a fast Fourier transform process on the vibration acceleration signal to generate a frequency domain signal includes: Determine a vibration acceleration signal segment based on a preset duration; Perform a fast Fourier transform on the vibration acceleration signal segment to generate a transformed frequency domain signal as the frequency domain signal.

6. The fault monitoring method according to claim 4, wherein The generating a peak information set and a weighted root mean square value based on the frequency domain signal includes: Sort each peak in the spectrum corresponding to the frequency domain signal to generate a peak sequence; Select a preset number of peaks from the peak sequence as target peaks to obtain a target peak set, and determine the peak information corresponding to each target peak in the target peak set to obtain a peak information set, wherein the peak information in the peak information set includes frequency, amplitude, and phase.

7. The fault monitoring method according to claim 6, wherein, The generating a peak information set and a weighted root mean square value based on the frequency domain signal further includes: Determine the signal length of the frequency domain signal and each node corresponding to the signal length; Determine at least one time domain acceleration component corresponding to each node to obtain a time domain acceleration component set, and determine the time domain acceleration sequence corresponding to the time domain acceleration component set; Perform a discrete Fourier transform on the time domain acceleration sequence based on the total acceleration and the signal length to generate frequency domain acceleration data; Based on the frequency-domain acceleration data, determine the power spectral density corresponding to the time-domain acceleration sequence; Based on a frequency weighting function, perform frequency weighting processing on the power spectral density to generate a weighted density; Based on the weighted density, generate a weighted root mean square value corresponding to the frequency-domain signal.

8. An electronic device, comprising: One or more processors; A storage device having stored thereon one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 4 to 7.

9. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, the method according to any one of claims 4 to 7 is implemented.

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