DE112020007629T5 - Method and device for detecting bearing errors based on the hearing properties of the human ear - Google Patents

Abstract

The present application relates to a method and a device for detecting bearing errors based on the hearing properties of the human ear. The method includes: a) obtaining a sound/vibration psychoacoustic annoyance model; b) determining the sound and/or vibration signals of a bearing; c) calculating psychoacoustic parameters on the basis of the determined sound and/or vibration signals; d) inputting the calculated psychoacoustic parameters into the obtained sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index; e) determining the bearing error class based on the obtained sound/vibration psychoacoustic annoyance index. The device includes a sensor and a processor.

Description

The present application relates to the field of storage technology. The present application relates specifically to a method and apparatus for detecting bearing defects.

Spindle bearings used in the machine tool industry play a key role in part machining accuracy. Therefore, bearing inspection before leaving the factory is particularly important for the operation and maintenance of machine tools. Spindle bearings are usually subjected to a basic vibration test before leaving the factory to check if the bearing is abnormal.

Currently, bearing failure detection is mainly based on simple vibration amplitude measurements and envelope demodulation or resonance demodulation methods. For example, in the Chinese patent specification CN 102840907B discloses a method for extraction and analysis of vibration signal characteristics of rolling bearings, in which method an envelope signal is obtained by interpolation resampling technology and its envelope spectrum is analyzed. The problem in practice, however, is that basic vibration noise measurements, such as measuring amplitude, RMS value, decibel value and other parameters, often cannot reflect the actual state of bearing operation. The method of vibration envelope demodulation often requires professional equipment for testing and analysis, and even requires post-processing of the collected data to obtain results, which is time-consuming and tedious.

In addition, human ears are often used to identify abnormal noise from bearings before the spindle bearings leave the factory, and then decide whether to release them. For experienced inspectors, the accuracy of bearing defect identification is high, but for less experienced inspectors, the accuracy of bearing defect identification is low. This subjective identification has a large variation and can easily lead to misidentification.

The object of the present application is therefore to provide a method and a device for detecting bearing errors, with which bearing errors can be identified objectively using the hearing properties of the human ear.

1. a) Erhalten eines Schall/Schwingung-Psychoakustik-Lästigkeitsmodells;
2. b) Ermitteln der Schall- und/oder Schwingungssignale eines Lagers;
3. c) Berechnen psychoakustischer Parameter auf der Grundlage der ermittelten Schall- und/oder Schwingungssignale;
4. d) Eingeben der berechneten psychoakustischen Parameter in das erhaltene Schall/Schwingung-Psychoakustik-Lästigkeitsmodell, um einen Schall/Schwingung-Psychoakustik-Lästigkeitsindex zu erhalten;
5. e) Bestimmen der Lagerfehlerklasse basierend auf dem erhaltenen Schall/Schwingung-Psychoakustik-Lästigkeitsindex.

On the one hand, the task is solved by a method for detecting bearing errors based on the hearing properties of the human ear. The registration process includes:

1. a) obtaining a sound/vibration psychoacoustic annoyance model;
2. b) determining the sound and/or vibration signals of a bearing;
3. c) calculating psychoacoustic parameters on the basis of the determined sound and/or vibration signals;
4. d) inputting the calculated psychoacoustic parameters into the obtained sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index;
5. e) determining the bearing error class based on the obtained sound/vibration psychoacoustic annoyance index.

In step a), a known psychoacoustic annoyance model, such as a universal psychoacoustic annoyance model, can be adopted directly for the sound/vibration psychoacoustic annoyance model.

Optionally and preferably, in particular in step a), the known psychoacoustic annoyance model can be optimized for the application of bearing error detection.

Herein the detection method particularly preferably further comprises: selecting a sample location and optimizing the sound/vibration psychoacoustic annoyance model using the error properties of the sample location. This enables a targeted model optimization in a simple way, especially before stock product recording. The detection method provided here is more sensitive to bearing errors and thus enables more effective detection of bearing errors.

It is advantageous here to be able to adjust the weighting of various psychoacoustic parameters in the sound/vibration psychoacoustic annoyance model using the error properties of the sample bed.

In the context of this application, the order of step a) relative to step b) and step c) is not specified. However, it is advantageous that before carrying out step b) a sound/vibration psychoacoustic annoyance model, preferably an optimized sound/vibration psychoacoustic annoyance model, has been obtained.

In step b), the camp is put into operation, for example using a test bench, with the Sound and / or vibration signals are determined by a sensor.

In a preferred embodiment, the sound and/or vibration signals are time domain signals.

In a preferred embodiment, the sound and/or vibration signals are acoustic pressure signals and/or vibration acceleration signals.

In a preferred embodiment, the psychoacoustic parameters include at least one of loudness, sharpness, roughness, and jitter. In this case, the psychoacoustic parameters involved, such as loudness, sharpness, roughness and/or fluctuation strength, can be selected application-specifically.

Advantageously, the psychoacoustic parameters include at least loudness and sharpness.

The psychoacoustic parameters particularly preferably include loudness, sharpness, roughness and fluctuation strength. Here, the recorded sound and/or vibration signals can be described more comprehensively with psychoacoustic parameters.

• c1) Erhalten eines psychoakustischen Parametermodells für jeden psychoakustischen Parameter;
• c2) Eingeben der erfassten Schall- und/oder Schwingungssignale jeweils in jedes psychoakustische Parametermodell, und jeweiliges Berechnen jedes charakteristischen psychoakustischen Parameters auf der Bark-Skala;
• c3) Integrieren jedes charakteristischen psychoakustischen Parameters auf der Bark-Skala, sodass jeder gesamte psychoakustische Parameter erhalten wird, wobei jeder gesamte psychoakustische Parameter verwendet werden kann, um in das Schall/Schwingung-Psychoakustik-Lästigkeitsmodell in Schritt d) eingegeben zu werden.

In an advantageous embodiment, in particular in step c), the detection method also includes:

• c1) obtaining a psychoacoustic parameter model for each psychoacoustic parameter;
• c2) inputting the detected sound and/or vibration signals into each psychoacoustic parameter model, respectively, and calculating each characteristic psychoacoustic parameter on the Bark scale, respectively;
• c3) Integrating each characteristic psychoacoustic parameter on the Bark scale to obtain each total psychoacoustic parameter, each total psychoacoustic parameter can be used to be input into the sound/vibration psychoacoustic annoyance model in step d).

It is particularly necessary first to establish a psychoacoustic parameter model for at least one of loudness, sharpness, roughness and fluctuation strength, preferably for loudness and sharpness, and particularly preferably for all four, or preferably to use a known psychoacoustic parameter model. Subsequently, the detected sound and/or vibration signals are respectively entered into each psychoacoustic parameter model so that the characteristic loudness, the characteristic sharpness, the characteristic roughness and/or the characteristic fluctuation strength on the Bark scale are obtained accordingly. By integrating the characteristic loudness, the characteristic sharpness, the characteristic roughness, and/or the characteristic jitter strength on the Bark scale, the overall loudness, sharpness, roughness, and/or jitter strength can be obtained respectively. The overall loudness, the overall sharpness, the overall roughness and/or the overall degree of fluctuation can be entered accordingly as input variables in the subsequent step d) in the sound/vibration psychoacoustics annoyance model, preferably the optimized sound/vibration psychoacoustics annoyance model.

In a preferred embodiment, a bearing error class evaluation criterion is created using a sound/vibration psychoacoustic annoyance model, in particular an optimized sound/vibration psychoacoustic annoyance model. For example, the bearing failure class is a division that depends on whether the operating condition of the bearing is normal or not. It is also possible to use the bearing error class to classify the bearing errors more precisely. The bearing error class evaluation criterion obtained in this way has the advantage of high objectivity, since it is created on the basis of the sound/vibration psychoacoustic annoyance model.

The bearing defect class is preferably determined here, in particular in step e), by comparing the sound/vibration psychoacoustic annoyance index obtained in step d) with the bearing defect class evaluation criterion. The sound/vibration psychoacoustic annoyance index, namely the VS-PA value (Vibration & Sound Psychoacoustic Annoyance) as the output variable of the sound/vibration psychoacoustic annoyance model is an objective variable based on the hearing properties of the human ear. In this case, the assessment of the bearing defect by comparing the VS-PA value with the objective bearing defect class evaluation criterion is also objective.

• einen Sensor, der zum Erfassen von Schall- und/oder Schwingungssignalen eines Lagers verwendet ist, und
• einen Prozessor, der verwendet ist, um Folgendes auszuführen:
  • Berechnen von psychoakustischen Parametern basierend auf den erfassten Schall- und/oder Schwingungssignalen,
  • Eingeben der berechneten psychoakustischen Parameter in das Schall/Schwingung-Psychoakustik-Lästigkeitsmodell, um einen Schall/Schwingung-Psychoakustik-Lästigkeitsindex zu erhalten, und
  • Bestimmen der Lagerfehlerklasse basierend auf dem Schall/Schwingung-Psychoakustik-Lästigkeitsindex.

On the other hand, the object of the present application is achieved by a device for detecting bearing errors based on the hearing properties of the human ear. The detection device includes:

• a sensor used to detect sound and/or vibration signals of a bearing, and
• a processor used to perform:
  • Calculation of psychoacoustic parameters based on the detected sound and/or vibration signals,
  • inputting the calculated psychoacoustic parameters into the sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index, and
  • Determining the bearing error class based on the sound/vibration psychoacoustic annoyance index.

In this case, the detection device starts to work when, for example, the bearing is put into operation using a test bench. The detection device here performs most of the steps of the above method for detecting bearing errors based on the hearing characteristics of the human ear.

A sound/vibration psychoacoustic annoyance model, in particular an optimized sound/vibration psychoacoustic annoyance model, is preferably stored in a memory unit of the detection device, in particular the processor of the detection device. Upon execution of a particular step, the processor may invoke the stored sound/vibration psychoacoustic annoyance model, particularly the optimized sound/vibration psychoacoustic annoyance model.

In a preferred embodiment, the device also includes an instruction device, the instruction device being used to output the bearing error class determined.

The instruction device is particularly preferably a display device, whereby the sound/vibration psychoacoustic annoyance index and/or the result of its comparison with the bearing error class evaluation criterion, i. H. the bearing defect class, can be displayed visually.

Alternatively, the instruction means is an acoustic output device, so that the tester can easily read through the sound the sound/vibration psychoacoustic annoyance index and/or the result of its comparison with the bearing fault class assessment criterion, i. H. the bearing defect class can be obtained.

It is conceivable that the detection device should also contain other necessary components, such as a power pack, etc.

In an advantageous embodiment, the detection device is designed as a hand-held device, which means that the bearing can be tested easily and flexibly and the test is largely unaffected by factors such as the position of the test stand of the bearing.

With the help of the method and device for detecting bearing defects provided in the application, it is possible to simulate the hearing properties of the human ear in order to record bearing operation and thus identify defective bearings. Since the sound/vibration psychoacoustic annoyance model is also optimized for bearing errors, the method provided here is highly sensitive to bearing errors, with which the bearing errors can be reliably recorded. The sound/vibration psychoacoustic annoyance model proposed here is objective and not influenced by subjective factors, so the reliability and stability are higher. Compared to the conventional method of spectrum analysis, inputting the bearing size and speed is not required to judge the bearing defects, so the structure of the detection device is simplified.

• 1 ein Blockdiagramm der grundlegenden Schritte eines Verfahrens zur Erfassung von Lagerfehlern gemäß einer Ausführungsform.
• 2 ein Blockdiagramm der Schritte zur Berechnung psychoakustischer Parameter gemäß einer Ausführungsform.
• 3 ein Blockdiagramm der Schritte zur Optimierung eines Schall/Schwingung-Psychoakustik-Lästigkeitsmodells gemäß einer Ausführungsform.
• 4 eine grafische Darstellung eines Lagerfehlerklasse-Bewertungskriteriums gemäß einer Ausführungsform.

The features, advantages and technical effects of exemplary embodiments of the present application are described below with reference to the drawings. It shows:

• 1 12 is a block diagram of the basic steps of a bearing failure detection method according to an embodiment.
• 2 12 is a block diagram of the steps for calculating psychoacoustic parameters according to one embodiment.
• 3 14 is a block diagram of the steps for optimizing a sound/vibration psychoacoustic annoyance model according to one embodiment.
• 4 a graphical representation of a bearing error class evaluation criterion according to an embodiment.

1 12 shows a block diagram of the basic steps of a method for detecting bearing defects according to an embodiment.

As in 1 shown, in the detection method according to the present embodiment, on the one hand, preferably a) obtaining a sound/vibration psychoacoustic annoyance model takes place first.

On the other hand, b) the sound and/or vibration signals of a bearing are determined, in particular by means of a sensor in the detection process direction. In the present embodiment, the sound and/or vibration signals are acoustic pressure signals or vibration acceleration signals in the time domain. Then, in particular with the aid of a processor in the detection device c), psychoacoustic parameters are calculated using the determined sound and/or vibration signals.

Then, in particular with the help of the processor in the detection device, d) the calculated psychoacoustic parameters are entered into the obtained sound/vibration psychoacoustic annoyance model, so that a sound/vibration psychoacoustic annoyance index (VS-PA value) is obtained, so that e) determining the bearing error class based on the received sound/vibration psychoacoustic annoyance index (VS-PA value).

With the aid of the detection method according to the present embodiment, the hearing properties of the human ear can be simulated in order to detect bearing operation and thus identify defective bearings. The detection method is not affected by subjective factors and has high objectivity, so the reliability and stability of detection are higher. In addition, compared to the conventional method of spectrum analysis, input of the bearing size and rotational speed is not required to judge the bearing defects, so the structure of the detection device is simplified.

2 12 shows a block diagram of the steps for calculating psychoacoustic parameters according to an embodiment. In the present embodiment, the psychoacoustic parameters include a total of four parameters, namely loudness, sharpness, roughness and jitter. Here the sound/vibration signals detected by the sensor in the detection device in the time domain, preferably the acoustic pressure signals or the vibration acceleration signals, are entered into the loudness model, the sharpness model, the roughness model and the fluctuation intensity model.

Here, the characteristic loudness of the sound/vibration signals on the Bark scale is obtained with the loudness model, and then the overall loudness can be obtained by integrating the characteristic loudness on the Bark scale. Similarly, the characteristic sharpness of the Bark-scale sound/vibration signals is obtained with the sharpness model, and then the overall sharpness can be obtained by integrating the characteristic sharpness of the Bark-scale sound/vibration signals. Similarly, the characteristic roughness of the sound/vibration signals on the Bark scale is obtained with the roughness model, and then the total roughness can be obtained by integrating the characteristic roughness on the Bark scale. Similarly, the characteristic fluctuation magnitude of the sound/vibration signals on the Bark scale is obtained with the fluctuation magnitude model, and then the total fluctuation magnitude can be obtained by integrating the characteristic fluctuation magnitude on the Bark scale. The resulting overall loudness, overall sharpness, overall roughness and/or overall fluctuation strength can be entered as input variables in subsequent steps in the sound/vibration psychoacoustics annoyance model, preferably the optimized sound/vibration psychoacoustics annoyance model.

3 12 shows a block diagram of the steps for optimizing the sound/vibration psychoacoustic annoyance model according to one embodiment. In the present embodiment, for example, a sample bed can be selected and an optimization variable can be selected using the error properties of the sample bed, and the universal or conventional sound/vibration psychoacoustic annoyance model can be optimized using a suitable optimization algorithm. Here, for example, the results of the analysis of the error properties of the sample bearing can be used to adjust the weighting of various psychoacoustic parameters in the sound/vibration psychoacoustic annoyance model. Here, for example, the weighting of loudness and sharpness can be increased accordingly and the weighting of roughness and fluctuation strength can be reduced accordingly. The detection method provided here is more sensitive to bearing errors and thus the bearing errors can be detected more effectively.

In addition, shows 3 that a bearing failure class evaluation criterion can be created using an optimized sound/vibration psychoacoustic annoyance model. The bearing error class evaluation criterion can be shown, for example, by the relationship between the sound/vibration psychoacoustic annoyance index (VS-PA value) and the bearing error class.

4 12 is a graphical representation of a bearing defect class evaluation criterion according to an embodiment.

In the present embodiment, the Sound/Vibration Psychoacoustic Annoyance Index (VS-PA value) determined by the Sound/Vibration Psychoacoustic Annoyance Model is preferable wise the optimized sound/vibration psychoacoustic annoyance model is obtained, in the range from 0 to 1. Here a possible bearing error class evaluation criterion can be determined from the sound/vibration psychoacoustic annoyance model, ie the bearing is regarded as normal if the VS-PA value is in the range of 0 to 0.4, the bearing is considered to be minor defective, if VS-PA value is in the range of 0.4 to 0.8, the bearing is considered to be severely defective , if the VS-PA value is in the range of 0.8 to 1.

In a further embodiment according to the present invention, an apparatus for detecting bearing errors based on the auditory characteristics of the human ear is provided. In the present embodiment, the detection device is designed as a hand-held device.

The sensing device includes a sensor, a processor, a commander, and a power supply that provides power to the sensor, processor, and commander. In the present embodiment, a preferably optimized sound/vibration psychoacoustic annoyance model is stored in a memory unit of the processor of the detection device.

The detection device starts to work when the bearing is put into operation using a test bench. First, the sound and/or vibration signals of the bearing are recorded with the sensor. Subsequently, the psychoacoustic parameters are calculated with the processor based on the detected sound and/or vibration signals, and the calculated psychoacoustic parameters are input into the sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index. Finally, the bearing failure class is determined based on the sound/vibration psychoacoustic annoyance index.

In the present embodiment, the sound/vibration psychoacoustic annoyance index and/or the result of its comparison with the bearing defect class evaluation criterion, i.e. the bearing defect class, can be output using the instructing means. The instruction device is preferably a display device through which the tester can read the sound/vibration psychoacoustic annoyance index and/or the bearing defect class. Alternatively or additionally, the instruction device is an acoustic output device, in which case the tester can also be informed, e.g. by speech, about the sound/vibration psychoacoustic annoyance index and/or the bearing error class.

With the method and apparatus for detecting bearing failures provided according to the embodiments contained herein, it is possible to simulate the auditory characteristics of the human ear in order to detect bearing operation and thus identify the defective bearings. Since the sound/vibration psychoacoustic annoyance model is optimized for bearing errors, the method provided here is highly sensitive to bearing errors, with which the bearing errors can be reliably detected. The sound/vibration psychoacoustic annoyance model proposed here is objective and not influenced by subjective factors, so the reliability and stability are higher. Compared to the conventional method of spectrum analysis, inputting the bearing size and speed is not required to judge the bearing defects, so the structure of the detection device is simplified.

Although the possible embodiments are described by way of example in the above description, it should be understood that there are still a large number of embodiment variants through a combination of all the technical features and embodiments known and easily thought of by technical personnel. It is also to be understood that the exemplary embodiments are only examples, and such embodiments in no way limit the scope, applications, and constructs of the present invention. Rather, the above description is intended to provide technical instruction for technicians implementing at least one exemplary embodiment, while various changes may be made, particularly in relation to the functional and structural aspects of the device, as long as it does not depart from the scope of the claims.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• CN 102840907B [0003]

Claims (15)

A method for detecting bearing errors based on the hearing characteristics of the human ear, comprising: a) obtaining a sound/vibration psychoacoustic annoyance model; b) determining the sound and/or vibration signals of a bearing; c) calculating psychoacoustic parameters on the basis of the determined sound and/or vibration signals; d) inputting the calculated psychoacoustic parameters into the obtained sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index; e) determining the bearing error class based on the obtained sound/vibration psychoacoustic annoyance index. procedure after claim 1 , characterized in that a sample bed is selected and the sound/vibration psychoacoustic annoyance model is optimized by means of the error properties of the sample bed. procedure after claim 2 , characterized in that the method comprises: adjusting the weighting of various psychoacoustic parameters in the sound/vibration psychoacoustic annoyance model based on the error properties of the sample bearing. Method according to one of the preceding claims, characterized in that the sound and/or vibration signals are time-domain signals. Method according to one of the preceding claims, characterized in that the sound and/or vibration signals are acoustic pressure signals and/or vibration acceleration signals. Method according to one of the preceding claims, characterized in that the psychoacoustic parameters comprise at least one of loudness, sharpness, roughness and fluctuation strength. procedure after claim 6 , characterized in that the psychoacoustic parameters include at least loudness and sharpness. procedure after claim 7 , characterized in that the psychoacoustic parameters include loudness, sharpness, roughness and fluctuation strength. Procedure according to one of Claims 6 until 8th , characterized in that the method further comprises: c1) obtaining a psychoacoustic parameter model for each psychoacoustic parameter; c2) inputting the detected sound and/or vibration signals into each psychoacoustic parameter model, respectively, and calculating each characteristic psychoacoustic parameter on the Bark scale, respectively; c3) respectively integrating each characteristic psychoacoustic parameter on the Bark scale, thereby obtaining each total psychoacoustic parameter, each total psychoacoustic parameter being used for input to the sound/vibration psychoacoustic annoyance model in step d). Method according to one of the preceding claims, characterized in that a bearing error class evaluation criterion is created with the sound/vibration psychoacoustic annoyance model. procedure after claim 10 , characterized in that the method also comprises: determining the bearing error class by comparing the sound/vibration psychoacoustic annoyance index with the bearing error class evaluation criterion. A device for detecting bearing errors based on the hearing characteristics of the human ear, the device comprising: a sensor used to detect sound and/or vibration signals of a bearing, and a processor used to perform: Calculation of psychoacoustic parameters based on the detected sound and/or vibration signals, inputting the calculated psychoacoustic parameters into the sound/vibration psychoacoustic annoyance model to obtain a sound/vibration psychoacoustic annoyance index, and Determining the bearing error class based on the sound/vibration psychoacoustic annoyance index. device after claim 11 , characterized in that the device further comprises an instruction device, wherein the instruction device is used to output the determined bearing error class. device after Claim 13 , characterized in that the instruction device is a display device. Device according to one of Claims 12 until 14 , characterized in that the device is constructed as a hand-held device.

Images