Fault detection method for self-adaptive noise reduction system of range hood and range hood

Abstract

The embodiment of the invention discloses a fault detection method for a self-adaptive noise reduction system of a range hood and the range hood. The fault detection method comprises the following steps: controlling a plurality of loudspeakers to sound in sequence; respectively acquiring a plurality of reference microphone sound pressure distribution signals acquired by each reference microphone when each loudspeaker sounds and a plurality of error microphone sound pressure distribution signals acquired by each error microphone when each loudspeaker sounds; and according to the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals, whether each loudspeaker, each reference microphone and each error microphone have a function fault is judged. According to the embodiment of the invention, the fault source can be accurately positioned by combining test signal excitation and sound pressure analysis, the problem that the microphone type cannot be distinguished due to the fact that a traditional detection method only depends on a single physical signal is solved, and the method is particularly suitable for a multi-microphone and multi-loudspeaker self-adaptive complex active noise control system and has a wide application prospect. Accurate fault detection of the self-adaptive noise reduction system in the range hood is realized.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of kitchen appliances, and particularly relates to a fault detection method of a self-adaptive noise reduction system of a range hood and the range hood. BACKGROUND

[0002] Active noise reduction is a noise control technology that can effectively reduce the level of middle and low frequency noise. An active noise reduction system usually includes a controller, a microphone and a speaker. The microphone can be divided into a reference microphone and an error microphone. The reference microphone is used to detect the noise signal propagated by the noise source, and the speaker generates an opposite sound wave according to the noise signal to cancel the noise. The error microphone is used to detect the noise signal after noise reduction, so as to realize self-adaptive active noise reduction. In order to ensure the noise reduction effect, the active noise reduction system usually sets multiple microphones and multiple speakers to form multiple noise reduction channels.

[0003] However, in the field of range hoods, when the active noise reduction module is set to actively reduce the noise of the range hood, if the microphone is installed incorrectly, the noise reduction effect of the active noise reduction system will be affected. In the prior art, there is a self-checking method for a non-self-adaptive noise reduction system, which detects the position of the microphone by single-frequency noise excitation. However, there are some limitations. For example, the microphone type cannot be distinguished, and the functional differences between the reference microphone (noise collection) and the error microphone (noise reduction effect feedback) are not distinguished. At the same time, the fault location is rough, and only whether the microphone is installed incorrectly can be judged, and other faults cannot be detected. SUMMARY

[0004] The present application provides a fault detection method of a self-adaptive noise reduction system of a range hood and the range hood, so as to accurately locate the fault source and realize accurate fault detection of a self-adaptive complex active noise control system with multiple microphones and multiple speakers.

[0005] In a first aspect, the embodiment of the present application provides a fault detection method of a self-adaptive noise reduction system of a range hood. The self-adaptive noise reduction system includes multiple reference microphones, multiple error microphones and multiple speakers. The fault detection method includes the following steps.

[0006] Controlling the multiple speakers to sound in turn;

[0007] Respectively acquiring multiple reference microphone sound pressure distribution signals collected by each reference microphone when each speaker sounds and multiple error microphone sound pressure distribution signals collected by each error microphone when each speaker sounds;

[0008] According to the multiple reference microphone sound pressure distribution signals and the multiple error microphone sound pressure distribution signals, it is judged whether each speaker, each reference microphone and each error microphone have a functional fault.

[0009] Optionally, based on the sound pressure distribution signals from multiple reference microphones and the sound pressure distribution signals from multiple error microphones, it is determined whether each speaker, each reference microphone, and each error microphone has a functional fault, including:

[0010] The root mean square (RMS) values ​​of sound pressure distribution signals from multiple reference microphones and multiple error microphones are calculated respectively to obtain multiple first RMS values ​​and multiple second RMS values.

[0011] Based on the difference between the first root mean square sound pressure level and the first root mean square threshold and / or the difference between the second root mean square sound pressure level and the second root mean square threshold, determine whether each speaker has a functional fault.

[0012] The difference between the first root mean square sound pressure level and the first root mean square threshold is used to determine whether each reference microphone has a functional malfunction.

[0013] The difference between the second root mean square sound pressure level and the second root mean square threshold is used to determine whether each error microphone has a functional malfunction.

[0014] Optionally, based on the difference between the first root mean square (RMS) sound pressure level and the first RMS threshold and / or the difference between the second RMS sound pressure level and the second RMS threshold, it is determined whether each speaker has a functional fault, including:

[0015] When the first root mean square value of sound pressure obtained by each reference microphone satisfies |SiRj-SR|>a, and / or when the second root mean square value of sound pressure obtained by each error microphone satisfies |SiEk-SE|>b, it is determined that the i-th speaker has a functional fault.

[0016] When the root mean square value of the first sound pressure level obtained by each reference microphone satisfies |SiRj-SR|≤a, and when the root mean square value of the second sound pressure level obtained by each error microphone satisfies |SiEk-SE|≤b, it is determined that the i-th speaker does not have a functional fault.

[0017] Wherein, SiRj is the first root mean square value of sound pressure obtained by the j-th reference microphone when the i-th speaker emits sound, SiEk is the second root mean square value of sound pressure obtained by the k-th error microphone when the i-th speaker emits sound, and i, j, and k are all positive integers greater than 1; SR is the first root mean square threshold, a is the first preset difference threshold, SE is the second root mean square threshold, and b is the second preset difference threshold.

[0018] Optionally, based on the difference between the first root mean square sound pressure level and the first root mean square threshold, it is determined whether each reference microphone has a functional malfunction, including:

[0019] determining that the jth reference microphone has a functional failure when the first sound pressure root mean square value of each speaker without functional failure satisfies |SiRj-SR| > a;

[0020] determining that the jth reference microphone has no functional failure when the first sound pressure root mean square value of each speaker without functional failure satisfies |SiRj-SR| ≤ a;

[0021] determining whether each error microphone has a functional failure according to the difference between the second sound pressure root mean square value and the second root mean square threshold, comprising:

[0022] determining that the kth error microphone has a functional failure when the second sound pressure root mean square value of each speaker without functional failure satisfies |SiEk-SE| > b;

[0023] determining that the kth error microphone has no functional failure when the second sound pressure root mean square value of each speaker without functional failure satisfies |SiEk-SE| ≤ b.

[0024] Optionally, after determining that the ith speaker has a functional failure when the first sound pressure root mean square value obtained by each reference microphone satisfies |SiRj-SR| > a and / or the second sound pressure root mean square value obtained by each error microphone satisfies |SiEk-SE| > b, the method further comprises:

[0025] outputting the component identifier and the failure type identifier of the ith speaker;

[0026] after determining that the jth reference microphone has a functional failure when the first sound pressure root mean square value of each speaker without functional failure satisfies |SiRj-SR| > a, the method further comprises:

[0027] outputting the component identifier and the failure type identifier of the jth reference microphone;

[0028] after determining that the kth error microphone has a functional failure when the second sound pressure root mean square value of each speaker without functional failure satisfies |SiEk-SE| > b, the method further comprises:

[0029] outputting the component identifier and the failure type identifier of the kth error microphone.

[0030] Optionally, the adaptive noise reduction system comprises a plurality of adaptive noise reduction subsystems, and each adaptive noise reduction subsystem comprises one speaker, one reference microphone and one error microphone;

[0031] After judging whether each loudspeaker, each reference microphone and each error microphone exist functional faults according to the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals, the method further comprises:

[0032] When each loudspeaker, each reference microphone and each error microphone do not exist functional faults, controlling the operation of the range hood;

[0033] Controlling each adaptive noise reduction subsystem to respectively execute an adaptive filtering algorithm to actively reduce noise during the operation of the range hood, and to judge a limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem in real time;

[0034] Judging whether each reference microphone exists a connection fault according to the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem.

[0035] Optionally, controlling each adaptive noise reduction subsystem to respectively execute an adaptive filtering algorithm to actively reduce noise during the operation of the range hood, and to judge a limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem in real time, comprises:

[0036] Controlling each adaptive noise reduction subsystem to respectively execute an adaptive filtering algorithm to actively reduce noise during the operation of the range hood, and to acquire a filter weight variation amount ||Δw|| and an error signal ||e(n)|| in real time during the execution of the adaptive filtering algorithm;

[0037] When the filter weight variation amount ||Δw|| is greater than a preset weight variation threshold and the error signal ||e(n)|| does not decrease for a first preset time, determining that the adaptive filtering algorithm is in a divergence state;

[0038] When the filter weight variation amount ||Δw|| is less than or equal to the preset weight variation threshold and the error signal ||e(n)|| decreases for the first preset time, determining that the adaptive filtering algorithm is in a convergence state.

[0039] Optionally, judging whether each reference microphone exists a connection fault according to the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem, comprises:

[0040] When the adaptive filtering algorithm executed by any one adaptive noise reduction subsystem is in the divergence state, determining that a reference microphone in the adaptive noise reduction subsystem exists a polarity reverse fault;

[0041] When the adaptive filtering algorithm executed by any one adaptive noise reduction subsystem is in the convergence state, determining that the reference microphone in the adaptive noise reduction subsystem does not exist a connection fault.

[0042] Optionally, after determining that the reference microphone in the adaptive noise reduction subsystem has a polarity reverse fault, when the adaptive filtering algorithm executed by any one of the adaptive noise reduction subsystems is in a divergent state, the method further comprises:

[0043] outputting the component identifier and the fault type identifier of the reference microphone in the adaptive noise reduction subsystem.

[0044] In a second aspect, the embodiments of the present application also provide a range hood, comprising an adaptive noise reduction system, the adaptive noise reduction system comprising a plurality of reference microphones, a plurality of error microphones, and a plurality of loudspeakers; the adaptive noise reduction system is configured to execute the fault detection method of the adaptive noise reduction system of the range hood according to any one of the first aspect.

[0045] The technical scheme of the embodiments of the present application can accurately locate the fault source by controlling the plurality of loudspeakers to sound in turn, acquiring a plurality of reference microphone sound pressure distribution signals through the reference microphones during the sounding process, acquiring a plurality of error microphone sound pressure distribution signals through the error microphones, and finally determining whether the loudspeakers, the reference microphones, and the error microphones have functional faults by using the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals. The technical scheme can combine the test signal excitation and the sound pressure analysis to accurately locate the fault source, solve the problem that the traditional detection method only relies on a single physical signal, and cannot distinguish the types of microphones, and is especially suitable for an adaptive complex active noise control system with multiple microphones and multiple loudspeakers, and can accurately detect faults of the adaptive noise reduction system in the range hood. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a front view of a range hood provided by the embodiments of the present application;

[0047] Figure 2 is Figure 1 a cross-sectional structural schematic view of the range hood structure shown in FIG. 1;

[0048] Figure 3 is Figure 1 a structural schematic view of an active noise reduction system in the range hood shown in FIG. 1;

[0049] Figure 4 and Figure 5 are structural schematic views of two disassembled parts of the active noise reduction system shown in FIG. 1; Figure 3

[0050] Figure 6 is a flowchart of a fault detection method of an adaptive noise reduction system of a range hood provided by the embodiments of the present application;

[0051] Figure 7 is a flowchart of another fault detection method of an adaptive noise reduction system of a range hood provided by the embodiments of the present application;

[0052] ​Figure 8 is a preset sound field distribution schematic diagram of a primary path and a secondary path of an adaptive noise reduction system provided by an embodiment of the present application;

[0053] Figure 9 is a flow chart of a fault detection method of a still another range hood adaptive noise reduction system provided by an embodiment of the present application;

[0054] Figure 10 is a flow chart of a fault detection method of a still another range hood adaptive noise reduction system provided by an embodiment of the present application.

[0055] In the figure:

[0056] 10 - smoke collecting cavity, 100 - smoke inlet, 20 - main cabinet, 21 - fan, 31 - reference microphone, 32 - error microphone, 33 - loudspeaker. DETAILED DESCRIPTION

[0057] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are intended to be merely illustrative of the present application and not in limitation thereof. It should also be noted that, for the purpose of description, only the parts related to the present application are shown in the drawings rather than all the parts.

[0058] The terms used in the embodiments of the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. It should be noted that the terms "upper", "lower", "left", "right", and the like described in the embodiments of the present application are described in the angle shown in the drawings and should not be understood as limiting the embodiments of the present application. In addition, it should be understood in the context that when referring to one element being formed "on" or "under" another element, it can be directly formed "on" or "under" another element, or indirectly formed "on" or "under" another element through an intermediate element. The terms "first", "second", and the like are merely for the purpose of description and do not represent any order, quantity, or importance, but are used to distinguish different components. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0059] The term "comprising" and its variants used in the present application are open and inclusive, i.e. "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment".

[0060] It should be noted that the concepts of "first", "second", and the like mentioned in the present application are merely used to distinguish the corresponding content and are not intended to limit the order or mutual dependency.

[0061] It should be noted that the modification of "one", "multiple" mentioned in the present application is illustrative but not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0062] Figure 1 is a front view of a range hood provided by an embodiment of the present application, Figure 2 is Figure 1 is a cross-sectional structure diagram of the range hood structure shown, Figure 3 is Figure 1 is a structure diagram of an active noise reduction system in the range hood shown, Figure 4 and Figure 5 are Figure 3 are structure diagrams of two disassembled parts of the active noise reduction system shown, and reference Figures 1-5 The range hood can be composed of a main box 20 and a smoke collecting cavity 10, wherein the smoke collecting cavity 10 is connected below the main box 20 and forms an air flow channel inside. The main box 20 is provided with a fan 21, and the smoke collecting cavity 10 is provided with a smoke inlet 100. When the range hood is working, the fan 21 is started, and the smoke inlet 100 forms a negative pressure, so as to absorb and discharge the oil fume generated in the cooking process of the range hood below to the outdoor.

[0063] It can be understood that since the fan 21 will vibrate and generate noise when working, the user's cooking experience will be affected. An adaptive noise reduction system can be provided in the range hood, and the adaptive noise reduction system includes multiple reference microphones 31, multiple error microphones 32 and multiple speakers 33. In the normal working state of the adaptive noise reduction system, when the range hood is running, the adaptive noise reduction system works synchronously, wherein the reference microphone 31 is responsible for detecting the noise signal generated by the noise source such as the fan 21. The speaker 33 plays the reverse sound wave according to the noise signal collected by the reference microphone 31, so as to offset the noise signal and realize active noise reduction. The error microphone 32 is responsible for real-time detection of the noise signal after active noise reduction. When the noise signal after active noise reduction does not meet the requirements, the reverse sound wave played by the speaker 33 is adjusted until the noise after active noise reduction meets the requirements, realizing the adaptive active noise reduction process.

[0064] For the above-mentioned range hood, the embodiments of the present application provide a variety of fault detection methods for the adaptive noise reduction system of the range hood, Figure 6 is a flowchart of a fault detection method for the adaptive noise reduction system of the range hood provided by an embodiment of the present application, and reference Figures 1-6 The fault detection method can be executed by the range hood provided by the embodiments of the present application, and specifically can be executed by the fault detection device therein. The fault detection device can be realized by software and / or hardware. The fault detection method can specifically include:

[0065] S110, control multiple speakers to sound in turn.

[0066] This step is an initial step of fault detection of the assembled range hood in the detection stage. The essence is to use the loudspeaker 33 in the range hood to make sound in turn to determine whether the loudspeaker 33 can normally play audio and whether there is a playing function fault. At the same time, it also provides a sound source for microphone fault detection in the adaptive noise reduction system.

[0067] S120, respectively acquiring a plurality of reference microphone sound pressure distribution signals collected by each reference microphone when each loudspeaker makes sound and a plurality of error microphone sound pressure distribution signals collected by each error microphone when each loudspeaker makes sound.

[0068] This step is a process of collecting sound pressure distribution signals at the positions of the reference microphones 31 and the error microphones 32 by using the reference microphones 31 and the error microphones 32 during the sound making process of each loudspeaker 33. Thus, each reference microphone 31 generates a reference microphone sound pressure distribution signal, and each error microphone 32 generates an error microphone sound pressure distribution signal. It should be noted that for the same loudspeaker 33, different position microphones collect specific sound field distribution signals associated with the positions during the audio playing process. According to the sound field distribution signals collected by the microphones at different positions, it can be determined whether the current playing loudspeaker 33 is faulty or the current microphone is faulty.

[0069] S130, judging whether each loudspeaker, each reference microphone and each error microphone has a function fault according to the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals.

[0070] This step is a process of specifically judging whether the components in the adaptive noise reduction system are normal by using the sound pressure distribution signals collected by the reference microphones 31 and the error microphones 32. It can be understood that when a loudspeaker 33 normally makes sound, each microphone can collect a specific sound field distribution signal. When each microphone cannot collect a specific sound field distribution signal, it indicates that the loudspeaker 33 has a sound making fault and cannot realize its own function. Further, when it is determined that the loudspeaker 33 normally makes sound, if a microphone cannot collect a specific sound field distribution signal, it indicates that the microphone has a sound collecting fault and cannot realize its own function.

[0071] The technical solutions of the above-mentioned embodiments control multiple loudspeakers to sound in turn, acquire multiple reference microphone sound pressure distribution signals through each reference microphone during the sounding process, acquire multiple error microphone sound pressure distribution signals through each error microphone, and finally determine whether each loudspeaker, each reference microphone and each error microphone has a functional fault by using the multiple reference microphone sound pressure distribution signals and the multiple error microphone sound pressure distribution signals. In combination with test signal excitation and sound pressure analysis, the fault source can be accurately located, the problem that a traditional detection method only relies on a single physical signal to cause the microphone type to be indistinguishable is solved, and the self-adaptive complex active noise control system with multiple microphones and multiple loudspeakers is particularly suitable for accurate fault detection of the self-adaptive noise reduction system in the range hood.

[0072] Figure 7 The flowchart of another fault detection method of a self-adaptive noise reduction system of a range hood provided by the embodiment of the present application is a refinement based on the previous embodiment. Specifically, for step S130, according to the multiple reference microphone sound pressure distribution signals and the multiple error microphone sound pressure distribution signals, determining whether each loudspeaker, each reference microphone and each error microphone has a functional fault, which can be refined as follows:

[0073] The sound pressure root mean square values of the multiple reference microphone sound pressure distribution signals and the multiple error microphone sound pressure distribution signals are calculated respectively to obtain multiple first sound pressure root mean square values and multiple second sound pressure root mean square values.

[0074] According to the difference between the first sound pressure root mean square value and the first root mean square threshold value and / or the difference between the second sound pressure root mean square value and the second root mean square threshold value, it is determined whether each loudspeaker has a functional fault.

[0075] According to the difference between the first sound pressure root mean square value and the first root mean square threshold value, it is determined whether each reference microphone has a functional fault.

[0076] According to the difference between the second sound pressure root mean square value and the second root mean square threshold value, it is determined whether each error microphone has a functional fault.

[0077] The details of the embodiment not yet described are referred to the above-mentioned embodiments.

[0078] As shown in FIG. 1, the fault detection method of the embodiment of the present application includes the following steps: Figure 7

[0079] S210, controlling multiple loudspeakers to sound in turn.

[0080] S220, acquiring multiple reference microphone sound pressure distribution signals collected by each reference microphone when each loudspeaker sounds and multiple error microphone sound pressure distribution signals collected by each error microphone when each loudspeaker sounds. ​

[0081] S231, respectively calculate the sound pressure root mean square values of the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals, to obtain a plurality of first sound pressure root mean square values and a plurality of second sound pressure root mean square values.

[0082] Figure 8 is a preset sound field distribution schematic diagram of the primary path and the secondary path of the adaptive noise reduction system provided by the embodiment of the application, with reference to Figure 8 , for example, the adaptive noise reduction system includes four loudspeakers (S1, S2, S3, S4), four reference microphones (R1, R2, R3, R4), and four error microphones (E1, E2, E3, E4). Under normal circumstances, when the loudspeaker S1 emits sound, the corresponding four reference microphones can respectively collect different reference microphone sound pressure distribution signals S1Rj, j takes 1, 2, 3, and 4, and the different reference microphone sound pressure distribution signals S1Rj are schematically and distinguished by white circles of different sizes in the figure. The corresponding four error microphones can respectively collect different error microphone sound pressure distribution signals S1Ek, k takes 1, 2, 3, and 4, and the different error microphone sound pressure distribution signals S1Ek are schematically and distinguished by black circles of different sizes in the figure. When the loudspeaker S2 emits sound, the corresponding different reference microphone sound pressure distribution signals S2Rj and different error microphone sound pressure distribution signals S2Ek can be obtained. Similarly, j takes 1, 2, 3, and 4, k takes 1, 2, 3, and 4, and so on. It can be known from Figure 8 that whether the loudspeaker or the microphone is abnormal can be judged by analyzing whether each reference microphone sound pressure distribution signal SiRj and each error microphone sound pressure distribution signal SiEk is normal, where i takes 1, 2, 3, and 4. Based on this, the step S231 quantifies the sound pressure distribution signals in the form of root mean square values to facilitate the analysis and judgment of the sound pressure distribution signals. In other words, the actual fluctuation of the sound pressure distribution signal can be represented by the corresponding root mean square value, which can facilitate the subsequent comparison and judgment process.

[0083] S232, judging whether each loudspeaker has a functional failure according to the difference between the first sound pressure root mean square value and the first root mean square threshold value and / or the difference between the second sound pressure root mean square value and the second root mean square threshold value.

[0084] Continuing to refer to Figure 8Specifically, the step can specifically include: determining that the i th loudspeaker has a functional failure when each of the first sound pressure root mean square values obtained by each reference microphone satisfies |SiRj-SR| > a, and / or when each of the second sound pressure root mean square values obtained by each error microphone satisfies |SiEk-SE| > b; determining that the i th loudspeaker does not have a functional failure when each of the first sound pressure root mean square values obtained by each reference microphone satisfies |SiRj-SR| ≤ a, and when each of the second sound pressure root mean square values obtained by each error microphone satisfies |SiEk-SE| ≤ b; wherein SiRj is a first sound pressure root mean square value obtained by the j th reference microphone when the i th loudspeaker emits sound, SiEk is a second sound pressure root mean square value obtained by the k th error microphone when the i th loudspeaker emits sound, i, j, and k are positive integers greater than 1; SR is a first root mean square threshold value, a is a first preset difference threshold value, SE is a second root mean square threshold value, and b is a second preset difference threshold value.

[0085] It can be understood that when a certain loudspeaker has a functional failure and cannot make a sound, all the reference microphone collected sound pressure distribution signals will be abnormal. Therefore, when it is judged that the difference absolute value between the reference microphone sound pressure root mean square value of the reference microphone collected sound pressure distribution signal, i.e., the first sound pressure root mean square value, and the preset first root mean square threshold SR is greater than a, it indicates that the difference is too large. At this time, it indicates that the reference microphone collected sound pressure distribution signal of each reference microphone is abnormal. Therefore, it can be determined that the loudspeaker has a functional failure. Similarly, when the difference absolute value between the error microphone sound pressure root mean square value of the error microphone collected sound pressure distribution signal, i.e., the second sound pressure root mean square value, and the preset second root mean square threshold SE is greater than b, it indicates that the difference is too large. At this time, it indicates that the error microphone collected sound pressure distribution signal of each error microphone is abnormal. Therefore, it can also be determined that the loudspeaker has a functional failure. It needs to be supplemented here that the abnormality of the sound pressure distribution signal collected by all the reference microphones can be used to judge the loudspeaker failure problem, or the abnormality of the sound pressure distribution signal collected by all the error microphones can be used to judge the loudspeaker failure problem. In order to exclude the false judgment of the loudspeaker failure caused by the failure of all the reference microphones or the failure of all the error microphones, the abnormality of the sound pressure distribution signal collected by all the reference microphones and the abnormality of the sound pressure distribution signal collected by all the error microphones can be combined to judge the loudspeaker failure problem, and the accuracy of the loudspeaker failure judgment can be improved. On the contrary, when the difference absolute value between each first sound pressure root mean square value and the preset first root mean square threshold SR is less than or equal to a, and when the difference absolute value between each second sound pressure root mean square value and the preset second root mean square threshold SE is less than or equal to b, it indicates that the reference microphone collected sound pressure distribution signal of each reference microphone and the error microphone collected sound pressure distribution signal of each error microphone are in a normal state. Therefore, it can be determined that the loudspeaker has no functional failure. It needs to be pointed out that a and b here are preset difference thresholds for judging whether the two kinds of sound pressure distribution signals are abnormal. The specific values can be obtained by pre-experiment, simulation, etc. The specific values are not limited here.

[0086] S233, judging whether each reference microphone has a functional failure according to the difference between the first sound pressure root mean square value and the first root mean square threshold.

[0087] S234, judging whether each error microphone has a functional failure according to the difference between the second sound pressure root mean square value and the second root mean square threshold.

[0088] With reference to the foregoing Figure 8Specifically, the step S233 can include: determining that the jth reference microphone has a functional failure when the first sound pressure root mean square values of each speaker in the absence of functional failure all satisfy |SiRj-SR| > a; and determining that the jth reference microphone does not have a functional failure when the first sound pressure root mean square values of each speaker in the absence of functional failure all satisfy |SiRj-SR| ≤ a. The step S234 can include: determining that the kth error microphone has a functional failure when the second sound pressure root mean square values of each speaker in the absence of functional failure all satisfy |SiEk-SE| > b; and determining that the kth error microphone does not have a functional failure when the second sound pressure root mean square values of each speaker in the absence of functional failure all satisfy |SiEk-SE| ≤ b.

[0089] It can also be understood that, for the step S233, each first sound pressure root mean square value represents the case of the reference microphone sound pressure distribution signal collected by each reference microphone, and under the premise that it is determined that the speaker does not have a functional failure through the step S232, when the absolute value of the difference between the first sound pressure root mean square value and the first root mean square threshold is greater than a, it indicates that the reference microphone sound pressure distribution signal is abnormal, and thus it can be determined that the reference microphone cannot effectively collect the corresponding sound pressure distribution signal and has a functional failure. Similarly, for the step S234, each second sound pressure root mean square value represents the case of the error microphone sound pressure distribution signal collected by each error microphone, and under the premise that it is determined that the speaker does not have a functional failure through the step S232, when the absolute value of the difference between the second sound pressure root mean square value and the second root mean square threshold is greater than b, it indicates that the error microphone sound pressure distribution signal is abnormal, and thus it can be determined that the error microphone cannot effectively collect the corresponding sound pressure distribution signal and has a functional failure.

[0090] It should be noted that the steps S233 and S234 are respectively used to determine the failure of the reference microphone and the error microphone, and the two do not affect each other, so the order can be changed, which is not limited here.

[0091] The scheme of the embodiment can not only determine the type of failure of the speaker or the microphone as a functional failure, but also effectively determine the type of component that has a functional failure as a speaker or a microphone, accurately locate the specific component that has a functional failure, and accurately locate the source and cause of the failure, greatly meeting the accurate failure detection needs of the adaptive complex active noise control system of the multi-microphone and multi-speaker.

[0092] On the basis of the above embodiment, further optionally, when the first sound pressure root mean square value obtained by each reference microphone satisfies |SiRj-SR|>a, and / or when the second sound pressure root mean square value obtained by each error microphone satisfies |SiEk-SE|>b, after determining that the i th loudspeaker has a functional failure, the following step can be additionally added: outputting the component identification and failure type identification of the i th loudspeaker.

[0093] When the first sound pressure root mean square value of each loudspeaker without functional failure satisfies |SiRj-SR|>a, after determining that the j th reference microphone has a functional failure, the following step can be additionally added: outputting the component identification and failure type identification of the j th reference microphone.

[0094] When the second sound pressure root mean square value of each loudspeaker without functional failure satisfies |SiEk-SE|>b, after determining that the k th error microphone has a functional failure, the following step can be additionally added: outputting the component identification and failure type identification of the k th error microphone.

[0095] Through the above additional steps, when it is determined that a functional failure occurs in the adaptive noise reduction system, the specific component identification and failure type identification of the functional failure can be output in time, so that the inspection personnel can perform timely maintenance, replacement and other operations, and the normal operation of the adaptive noise reduction system of the range hood is ensured.

[0096] Figure 9 is a flowchart of another fault detection method of a range hood adaptive noise reduction system provided by the embodiment of the present application, which is optimized on the basis of the above embodiment. First, the reference Figures 1-5 In the range hood to which the present embodiment is directed, the adaptive noise reduction system includes a plurality of adaptive noise reduction subsystems, and each adaptive noise reduction subsystem includes a loudspeaker 33, a reference microphone 31 and an error microphone 32.

[0097] On this basis, in the present embodiment, after the step S130 of the above embodiment, whether each loudspeaker, each reference microphone and each error microphone has a functional failure is judged according to the sound pressure distribution signals of the plurality of reference microphones and the sound pressure distribution signals of the plurality of error microphones, the following content can be additionally added:

[0098] When each loudspeaker, each reference microphone and each error microphone does not have a functional failure, the range hood is controlled to run;

[0099] Each adaptive noise reduction subsystem is controlled to execute an adaptive filtering algorithm to actively reduce the noise in the running process of the range hood, and the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem is judged in real time;

[0100] According to the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem, it is judged whether there is a connection fault of each reference microphone.

[0101] The details of the embodiments not described above can refer to the embodiments described above.

[0102] As Figure 9 shown, the fault detection method of the embodiment of the application comprises the following steps:

[0103] S310, control the plurality of loudspeakers to sound in turn.

[0104] S320, respectively acquire a plurality of reference microphone sound pressure distribution signals collected by each reference microphone when each loudspeaker sounds and a plurality of error microphone sound pressure distribution signals collected by each error microphone when each loudspeaker sounds.

[0105] S330, according to the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals, it is judged whether there is a functional fault of each loudspeaker, each reference microphone and each error microphone.

[0106] S340, when there is no functional fault of each loudspeaker, each reference microphone and each error microphone, control the range hood to run.

[0107] S350, control each adaptive noise reduction subsystem to execute an adaptive filtering algorithm respectively to actively reduce the noise in the running process of the range hood, and judge the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem in real time.

[0108] S360, according to the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem, it is judged whether there is a connection fault of each reference microphone.

[0109] First of all, it needs to be explained that the above steps S340-S350 are further fault judgments under the premise that the self-adaptive noise reduction system of the smoke machine is confirmed to have no functional failure of each component in S330, and the main purpose is to test the connection problem of the reference microphone. It can be understood that the self-adaptive noise reduction system needs to accurately collect the noise signal generated when the smoke machine is running through the reference microphone, and on the basis of the accurate noise signal obtained, the inverse sound wave signal is played through the loudspeaker to superimpose with the noise signal, and the error microphone is used to detect the superposition effect and then adaptively feedback adjust the inverse sound wave signal, thereby ultimately realizing the cancellation effect of the noise. However, in addition to functional failure, the reference microphone also has a connection error, that is, the polarity is reversed, which will also affect the above-mentioned self-adaptive noise reduction process, resulting in the failure of active noise reduction. The principle is that when the polarity of the reference microphone is reversed, the sound pressure distribution signal generated by the reference microphone is opposite to the actual sound pressure distribution signal, and when the active noise reduction is performed with the inverse sound pressure distribution signal, it cannot be superimposed and cancelled with the actual noise, resulting in the failure of the adaptive process.

[0110] Based on this, the above steps S340-S350 are essentially a process of performing adaptive active noise reduction experiments using the self-adaptive noise reduction system, wherein one reference microphone, one loudspeaker, and one error microphone form a set of adaptive noise reduction subsystems, and each adaptive noise reduction subsystem performs an adaptive active noise reduction process according to an adaptive filtering algorithm. As can be known by those skilled in the art, when the adaptive filtering algorithm completes convergence in a limit state, it means that effective adaptive active noise reduction is achieved, that is, the adaptive noise reduction subsystem is functioning normally and there is no connection failure problem with the reference microphone. On the contrary, when the adaptive filtering algorithm cannot complete convergence in a limit state, it means that effective adaptive active noise reduction cannot be achieved, that is, the adaptive noise reduction subsystem is malfunctioning, and at this time it can be determined that there is a connection failure problem with the reference microphone in the corresponding adaptive noise reduction subsystem, causing the adaptive active noise reduction to malfunction.

[0111] The scheme of the present embodiment not only judges whether each loudspeaker, each reference microphone, and each error microphone has a functional failure through the sound pressure distribution signal of the reference microphone and the sound pressure distribution signal of the error microphone, but also performs adaptive active noise reduction experiments on each adaptive noise reduction subsystem. In this way, it can be determined whether the reference microphone in each adaptive noise reduction subsystem has a connection failure, thereby realizing accurate judgment of various faults and fault source positioning in a multi-microphone and multi-loudspeaker adaptive complex active noise control system through the combination of test signal excitation, sound pressure analysis, and adaptive filtering algorithm control, and improving the accuracy and efficiency of fault detection.

[0112] Figure 10is a flowchart of another fault detection method of the self-adaptive noise reduction system of the range hood provided by the embodiment of the present application, which is based on the above embodiment and is more detailed, in which, for the step S350 of the above embodiment, the adaptive filtering algorithm is executed by each adaptive noise reduction subsystem respectively to actively reduce the noise in the operation of the range hood, and the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem is determined in real time, which can be further detailed as:

[0113] the adaptive filtering algorithm is executed by each adaptive noise reduction subsystem respectively to actively reduce the noise in the operation of the range hood, and the filter weight variation ||Δw|| and the error signal ||e(n)|| in the execution process of the adaptive filtering algorithm are obtained in real time;

[0114] when the filter weight variation ||Δw|| is greater than the preset weight variation threshold and the error signal ||e(n)|| does not decrease within the first preset time, it is determined that the adaptive filtering algorithm is in a divergence state;

[0115] when the filter weight variation ||Δw|| is less than or equal to the preset weight variation threshold and the error signal ||e(n)|| decreases within the first preset time, it is determined that the adaptive filtering algorithm is in a convergence state.

[0116] for the step S360 of the above embodiment, according to the limit state of the adaptive filtering algorithm executed by each adaptive noise reduction subsystem, whether there is a connection fault of the reference microphone can be determined, which can be further detailed as:

[0117] when the adaptive filtering algorithm executed by any adaptive noise reduction subsystem is in a divergence state, it is determined that the reference microphone in the adaptive noise reduction subsystem has a polarity reverse fault;

[0118] when the adaptive filtering algorithm executed by any adaptive noise reduction subsystem is in a convergence state, it is determined that the reference microphone in the adaptive noise reduction subsystem does not have a connection fault.

[0119] The details of the embodiment not described above can be referred to the previous embodiment.

[0120] As shown in Figure 10 the fault detection method of the embodiment of the present application includes the following steps:

[0121] S410, control the plurality of loudspeakers to sound in turn.

[0122] S420, respectively acquire a plurality of reference microphone sound pressure distribution signals collected by each reference microphone when each loudspeaker sounds and a plurality of error microphone sound pressure distribution signals collected by each error microphone when each loudspeaker sounds.

[0123] S430, determining whether each loudspeaker, each reference microphone and each error microphone has a functional failure according to the plurality of reference microphone sound pressure distribution signals and the plurality of error microphone sound pressure distribution signals.

[0124] S440, controlling the operation of the range hood when each loudspeaker, each reference microphone and each error microphone does not have a functional failure.

[0125] S451, controlling each adaptive noise reduction subsystem to respectively execute an adaptive filtering algorithm to actively reduce noise during the operation of the range hood, and to obtain a filter weight change amount || Dw || and an error signal || e(n) || in real time during the execution of the adaptive filtering algorithm.

[0126] As can be appreciated by those skilled in the art, during the execution of the adaptive filtering algorithm, the adaptive filter W(z) is adjusted in feedback according to the error signal e(n) to minimize the error signal e(n), thereby ultimately ensuring the best active noise reduction effect. The feedback adjustment mainly updates the filter weight, and by gradually adjusting the filter weight, the error signal e(n) detected by the error microphone gradually decreases, i.e., convergence is achieved, and ultimately a stable active noise reduction state is reached. Therefore, it can be known that the filter weight change amount || Dw || and the error signal || e(n) || detected by the error microphone can directly reflect the limit state of the adaptive filtering algorithm, indicating whether the adaptive noise reduction subsystem has completed convergence. This step is a process of obtaining these two parameters reflecting the limit state of the adaptive filtering algorithm in real time during the execution of the adaptive filtering algorithm, to facilitate subsequent analysis and judgment of the adaptive filtering experimental results.

[0127] S452, when the filter weight change amount || Dw || is greater than the preset weight change threshold and the error signal || e(n) || does not decrease for a first preset time, determining that the adaptive filtering algorithm is in a divergence state.

[0128] S453, when the filter weight change amount || Dw || is less than or equal to the preset weight change threshold and the error signal || e(n) || decreases for a first preset time, determining that the adaptive filtering algorithm is in a convergence state.

[0129] The steps S452 and S453 are specific processes for determining the limit state of the adaptive filtering algorithm by using the filter weight change amount ||Dw|| and the error signal ||e(n)||. When the filter weight change amount ||Dw|| is greater than a preset weight change threshold, for example, 0.2, for a certain period of time, it indicates that the filter weight changes greatly in the period of time, and the adaptive feedback adjustment amplitude is large. At the same time, when the error signal ||e(n)|| does not effectively decrease, it indicates that the adaptive feedback adjustment does not achieve the target result of minimizing the error signal e(n) in the period of time. In other words, at this time, it can be determined that the adaptive filtering algorithm does not complete convergence and is in a divergence state. Conversely, it can be determined that the adaptive filtering algorithm has completed convergence and is in a convergence state.

[0130] S461, when the adaptive filtering algorithm executed by any one of the adaptive noise reduction subsystems is in a divergence state, it is determined that the reference microphone in the adaptive noise reduction subsystem has a polarity reverse connection fault.

[0131] S462, when the adaptive filtering algorithm executed by any one of the adaptive noise reduction subsystems is in a convergence state, it is determined that the reference microphone in the adaptive noise reduction subsystem does not have a connection fault.

[0132] As shown in the foregoing, when the adaptive filtering algorithm limit state is in a divergence state and cannot complete convergence, it indicates that the active noise reduction cannot be effectively adapted, that is, the adaptive noise reduction subsystem is malfunctioning. At this time, it can be determined that the reference microphone in the adaptive noise reduction subsystem has a connection fault. Conversely, it can be determined that the adaptive noise reduction subsystem is functioning normally and does not have a reference microphone connection fault. On the basis of the foregoing embodiment, further optionally, after it is determined that the reference microphone in the adaptive noise reduction subsystem has a polarity reverse connection fault when the adaptive filtering algorithm executed by any one of the adaptive noise reduction subsystems is in a divergence state, the following step can be additionally provided: outputting the component identification and fault type identification of the reference microphone in the adaptive noise reduction subsystem.

[0133] By the additional step, when it is determined that each adaptive noise reduction subsystem cannot complete convergence, it is determined that the reference microphone in the adaptive noise reduction subsystem has a polarity reverse connection and has a connection fault, and the specific component identification and fault type identification of the connection fault are outputted in time, so that the re-wiring operation of the inspection personnel is facilitated, and the normal operation of the adaptive noise reduction system of the smoke machine is ensured.

[0134] Note that the above merely describes preferred embodiments of the present application and the principles of the technology applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, modifications, combinations and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A fault detection method for a range hood adaptive noise reduction system, characterized in that, The adaptive noise reduction system includes multiple reference microphones (31), multiple error microphones (32), and multiple speakers (33); the fault detection method includes: Control the multiple speakers to emit sound sequentially; Multiple reference microphone sound pressure distribution signals and multiple error microphone sound pressure distribution signals are acquired by each of the reference microphones when each of the speakers emits sound. Based on the sound pressure distribution signals of the multiple reference microphones and the sound pressure distribution signals of the multiple error microphones, determine whether each of the loudspeakers, each of the reference microphones and each of the error microphones has a functional fault.

2. The fault detection method according to claim 1, characterized in that, Based on the sound pressure distribution signals from the plurality of reference microphones and the sound pressure distribution signals from the plurality of error microphones, determine whether each of the loudspeakers, reference microphones, and error microphones has a functional malfunction, including: The root mean square (RMS) values ​​of the sound pressure distribution signals of the multiple reference microphones and the multiple error microphones are calculated respectively to obtain multiple first RMS values ​​and multiple second RMS values. Based on the difference between the first root mean square sound pressure level and the first root mean square threshold and / or the difference between the second root mean square sound pressure level and the second root mean square threshold, determine whether each of the speakers has a functional fault. Based on the difference between the first root mean square sound pressure value and the first root mean square threshold, it is determined whether each of the reference microphones has a functional malfunction. Based on the difference between the second root mean square sound pressure level and the second root mean square threshold, it is determined whether each of the error microphones has a functional malfunction.

3. The fault detection method according to claim 2, characterized in that, Based on the difference between the first root mean square (RMS) sound pressure level and the first RMS threshold and / or the difference between the second RMS sound pressure level and the second RMS threshold, determine whether each of the speakers has a functional malfunction, including: When the root mean square value of the first sound pressure level obtained by each of the reference microphones satisfies |SiRj-SR|>a, and / or when the root mean square value of the second sound pressure level obtained by each of the error microphones satisfies |SiEk-SE|>b, it is determined that the i-th speaker has a functional fault. When the root mean square value of the first sound pressure level obtained by each of the reference microphones satisfies |SiRj-SR|≤a, and when the root mean square value of the second sound pressure level obtained by each of the error microphones satisfies |SiEk-SE|≤b, it is determined that the i-th speaker does not have a functional fault. Wherein, SiRj is the first root mean square value of sound pressure obtained by the j-th reference microphone when the i-th speaker emits sound, SiEk is the second root mean square value of sound pressure obtained by the k-th error microphone when the i-th speaker emits sound, and i, j, and k are all positive integers greater than 1; SR is the first root mean square threshold, a is the first preset difference threshold, SE is the second root mean square threshold, and b is the second preset difference threshold.

4. The fault detection method according to claim 3, characterized in that, Based on the difference between the first root mean square sound pressure level and the first root mean square threshold, determine whether each of the reference microphones has a functional malfunction, including: When the first root mean square value of the sound pressure level is satisfied when all the speakers without functional failures emit sound, the j-th reference microphone is determined to have a functional failure. When the first root mean square value of the sound pressure level is satisfied |SiRj-SR|≤a when each of the speakers without functional failures emits sound, it is determined that the j-th reference microphone does not have a functional failure. Based on the difference between the second root mean square sound pressure level and the second root mean square threshold, determine whether each of the error microphones has a functional malfunction, including: When the second root mean square value of sound pressure is satisfied |SiEk-SE|>b when each of the speakers without functional failure is emitting sound, it is determined that the kth error microphone has a functional failure. When the second root mean square value of sound pressure is satisfied |SiEk-SE|≤b when each of the speakers without functional failures emits sound, it is determined that the k-th error microphone does not have a functional failure.

5. The fault detection method according to claim 4, characterized in that, After determining that the i-th speaker has a functional fault, the method further includes: when the first root mean square value of sound pressure obtained by each of the reference microphones satisfies |SiRj-SR|>a, and / or when the second root mean square value of sound pressure obtained by each of the error microphones satisfies |SiEk-SE|>b. Output the component identifier and fault type identifier of the i-th speaker; After determining that the j-th reference microphone has a functional fault, when all the speakers without functional faults emit sound, the root mean square value of the first sound pressure level satisfies |SiRj-SR|>a, the method further includes: Output the component identifier and fault type identifier of the j-th reference microphone; After determining that the k-th error microphone has a functional fault, when all the speakers without functional faults emit sound, the second root mean square value of sound pressure all satisfy |SiEk-SE|>b, the method further includes: Output the component identifier and fault type identifier of the k-th error microphone.

6. The fault detection method according to claim 1, characterized in that, The adaptive noise reduction system includes multiple adaptive noise reduction subsystems, each of which includes a speaker (33), a reference microphone (31), and an error microphone (32). After determining whether each of the loudspeakers, reference microphones, and error microphones has a functional malfunction based on the sound pressure distribution signals of the plurality of reference microphones and the sound pressure distribution signals of the plurality of error microphones, the method further includes: When none of the speakers, reference microphones, and error microphones are malfunctioning, the smoke machine is controlled to operate. Each of the adaptive noise reduction subsystems is controlled to execute an adaptive filtering algorithm to actively reduce the noise during the operation of the smoke machine, and the extreme state of the adaptive filtering algorithm executed by each of the adaptive noise reduction subsystems is judged in real time. Based on the limit state of the adaptive filtering algorithm executed by each of the adaptive noise reduction subsystems, it is determined whether each of the reference microphones has a connection fault.

7. The fault detection method according to claim 6, characterized in that, Each of the adaptive noise reduction subsystems is controlled to execute an adaptive filtering algorithm to actively reduce noise during the operation of the smoke machine, and the extreme states of the adaptive filtering algorithms executed by each of the adaptive noise reduction subsystems are determined in real time, including: Each of the adaptive noise reduction subsystems is controlled to execute an adaptive filtering algorithm to actively reduce the noise during the operation of the smoke machine, and the filter weight change ‖Δw‖ and error signal ‖e(n)‖ are obtained in real time during the execution of the adaptive filtering algorithm. If the change in filter weights ‖Δw‖ is greater than a preset weight change threshold for a continuous first preset time period, and the error signal ‖e(n)‖ does not decrease, the adaptive filtering algorithm is determined to be in a divergent state. If, during the first preset time period, the change in filter weights ‖Δw‖ is less than or equal to the preset weight change threshold, and the error signal ‖e(n)‖ decreases, the adaptive filtering algorithm is determined to be in a convergent state.

8. The fault detection method according to claim 6, characterized in that, Based on the extreme states of the adaptive filtering algorithms executed by each of the adaptive noise reduction subsystems, determine whether each of the reference microphones has a connection fault, including: When the adaptive filtering algorithm executed by any of the adaptive noise reduction subsystems is in a divergent state, it is determined that the reference microphone in the adaptive noise reduction subsystem has a polarity reversal fault. When the adaptive filtering algorithm executed by any of the adaptive noise reduction subsystems is in a convergent state, it is determined that the reference microphone in the adaptive noise reduction subsystem does not have a connection fault.

9. The fault detection method according to claim 8, characterized in that, When the adaptive filtering algorithm executed by any of the adaptive noise reduction subsystems is in a divergent state, after determining that the reference microphone in the adaptive noise reduction subsystem has a polarity reversal fault, the method further includes: Output the component identifier and fault type identifier of the reference microphone in the adaptive noise reduction subsystem.

10. A range hood, characterized in that, The system includes an adaptive noise reduction system comprising multiple reference microphones (31), multiple error microphones (32), and multiple speakers (33); the adaptive noise reduction system is used to perform a fault detection method for the adaptive noise reduction system of a smoke hood as described in any one of claims 1-9.

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