SYSTEM AND METHOD FOR MONITORING A MACHINE

A system for monitoring a machine includes a transducer mounted to the machine, and a processing unit coupled to the transducer. The transducer converts a sound produced by the machine during operation into a to-be-tested dataset. The processing unit receives the to-be-tested dataset from the transducer, performs time-frequency analysis on the to-be-tested dataset to generate a to-be-tested spectrogram based on the to-be-tested dataset, inputs the to-be-tested spectrogram to an analysis model of a deep neural network to obtain an analysis result, determines whether the machine is abnormal based on the analysis result, and outputs an abnormal signal when it is determined that the machine is abnormal.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Taiwanese Invention Patent Application No. 109137390, filed on Oct. 28, 2020.

FIELD

The disclosure relates to a system and a method for monitoring a machine, and more particularly to a system and a method for monitoring a machine according to a sound produced by operation of the machine.

BACKGROUND

Highly automated machinery is becoming increasingly widespread. However, all machines may potentially malfunction after extended use. If a malfunction of a machine cannot be detected and resolved in time, it could negatively affect efficiency of the machine, e.g., production yield for factory machinery, and decrease useful lifespan of the machine.

A conventional method for detecting an abnormal state of a machine requires disassembling the machine in order to identify malfunctioning components. Therefore, most factory machinery must either rely on routine service or wait until the machine malfunctions before calling for repairs.

The problem with routine service is that a machine may be able to operate with a defective component for some time before the defect is found through servicing. During this period of operation, the defective component may cause damage to other components within the machine, thereby increasing the cost of repairing the machine. Furthermore, if the defective component is not identified during servicing and the machine is left to operate until it ultimately malfunctions, the cost of repair would increase significantly.

SUMMARY

Therefore, an object of the disclosure is to provide a system and a method for monitoring a machine that can alleviate at least one of the drawbacks of the prior art.

According to one aspect of the disclosure, a system for monitoring a machine includes a transducer and a processing unit. The transducer is configured to be mounted to a target machine and to convert a sound produced by the target machine during operation into a to-be-tested dataset. The processing unit is coupled to the transducer to receive the to-be-tested dataset, and is configured to perform time-frequency analysis on the to-be-tested dataset to generate a to-be-tested spectrogram based on the to-be-tested dataset, to input the to-be-tested spectrogram to an analysis model of a deep neural network to obtain an analysis result, to determine whether the target machine is abnormal based on the analysis result, and to output an abnormal signal when it is determined that the target machine is abnormal.

According to another aspect of the disclosure, a method for monitoring a machine is to be implemented by a processing unit, and includes steps of receiving a to-be-tested dataset that is related to a sound produced by operation of a target machine, performing time-frequency analysis on the to-be-tested dataset to generate a to-be-tested spectrogram based on the to-be-tested dataset, inputting the to-be-tested spectrogram to an analysis model of a deep neural network to obtain an analysis result, determining whether the target machine is abnormal based on the analysis result, and outputting an abnormal signal when it is determined that the target machine is abnormal.

DETAILED DESCRIPTION

Throughout the disclosure, the term “coupled to” may refer to a direct connection among a plurality of electrical apparatus/devices/equipments via an electrically conductive material (e.g., an electrical wire), or an indirect connection between two electrical apparatus/devices/equipments via another one or more apparatus/device/equipment, or wireless communication between two electrical apparatus/devices/equipments via a communication network.

Referring toFIG. 1, an embodiment of a system1for monitoring a machine includes a transducer11, a storage unit12, a processing unit13and an output unit14.

The transducer11(e.g., a microphone) is mounted to a target machine10(e.g., a machine tool) and is configured to convert a sound produced by operation of the target machine10into an audio dataset. In this embodiment, the transducer11is configured to convert a sound having an audio frequency between 20 Hz and 48 kHz or above 48 kHz. Since the system1may be used to monitor the target machine10over a long period of time, the transducer11may be configured to capture and convert, for example,10seconds of sound into an audio dataset every one minute in order to save power consumption. It should be noted that this disclosure is not limited to the above-mentioned configuration of the transducer11.

The storage unit12is, for example but not limited to, electrically-erasable programmable read-only memory (EEPROM), a hard disk, a solid-state drive (SSD), or a non-transitory storage medium (e.g., secure digital (SD) memory, flash memory, etc.). The storage unit12is electrically connected to the processing unit13, and stores instructions that are executable by the processing unit13to implement a method for monitoring a machine. Examples of the instructions may include any suitable types of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, object-oriented code, visual code, and the like. In this embodiment, the storage unit12further stores a plurality of training datasets that are related to sounds produced by the target machine10or by another machine of a same type as the target machine10during normal operation. Specifically, the plurality of training datasets may be generated by the transducer11capturing sounds produced by the target machine10at different time points during normal operation or produced by one or more other machines of the same type as the target machine10at different time points during normal operation, and then converting the sounds thus captured into a plurality of audio datasets that serve as the plurality of training datasets, respectively. It should be noted that the sounds produced during normal operation of said machine(s) and captured by the transducer11may have an audio frequency between 20 Hz and 48 kHz or above 48 kHz.

For example, the processing unit13is a microcontroller including, but not limited to, a single core processor, a multi-core processor, a dual-core mobile processor, a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), etc. The processing unit13is configured to wirelessly communicate with the transducer11through a communication network100and is coupled to the storage unit12. In some embodiments, the processing unit13may be electrically connected to the transducer11through a wired connection. In some embodiments, the processing unit13and the storage unit12are included in a single computing device (e.g., a server, a personal computer, a laptop computer, a tablet computer, etc.). In some embodiments, the processing unit13is included in one server while the storage unit12is included in another server (e.g., a database server) that communicates with said one server through a communication network.

The output unit14is coupled to the processing unit13. In some embodiments, the output unit14may be embodied using a display device or a speaker that is electrically connected to and controlled by the processing unit13. In some embodiments, the output unit14may be embodied using a personal electronic device (e.g., a smart phone, a tablet computer, etc.) communicating with the processing unit13through a communication network to receive a signal therefrom.

The transducer11and the processing unit13may each include a communication component (e.g., a radio-frequency integrated circuit (RFIC), a short-range wireless communication module supporting a short-range wireless communication network using a wireless technology of Bluetooth® and/or Wi-Fi, etc., a mobile communication module supporting telecommunication using Long-Term Evolution (LTE), the third generation (3G) and/or fifth generation (5G) of wireless mobile telecommunications technology, or the like), allowing the transducer11and the processing unit13to wirelessly communicate with each other.

Further referring toFIGS. 2 to 4, the method for monitoring a machine includes a training procedure2, a threshold-determining procedure3and a monitoring procedure4.

In step21of the training procedure2, the processing unit13performs time-frequency analysis on the plurality of training datasets to generate a plurality of training spectrograms based respectively on the plurality of training datasets.

Then, in step22of the training procedure2, the processing unit13inputs the plurality of training spectrograms to a convolutional neural network (CNN) model to train the CNN model. In some embodiments, the CNN model may be a pre-trained model (e.g., Autoencoder, Densenet, Xception and Resnet). The CNN model that has been trained using the plurality of training spectrograms will be used as an analysis model of a deep neural network in the monitoring procedure4for determining whether the target machine10is abnormal. It should be noted that an output of the analysis model is a value ranging from 0 to 100 and indicating a probability of an input spectrogram into the analysis model belonging to a class defined by the plurality of training spectrograms (a class of normal operation). In other words, the output of the analysis model means a similarity between the input spectrogram and the group of training spectrograms. Specifically, the greater the value of the output of the analysis model, the more similar the input spectrogram is to the group of training spectrograms, which means that a sound related to the input spectrogram is more similar than not to the sounds related to the group of training spectrograms.

After the analysis model is built, the processing unit13implements the threshold-determining procedure3that includes steps31-33to determine a threshold value to be used in the monitoring procedure4.

In step31, for each of the plurality of training spectrograms, the processing unit13inputs the training spectrogram to the analysis model to obtain a reference value that indicates similarity between the training spectrogram and the group of training spectrograms.

Then, the processing unit13calculates an average and a standard deviation of the reference values that are respectively obtained in step31for the plurality of training spectrograms (step32), and obtains a threshold value based on the average and the standard deviation (step33). For example, in step33, the processing unit13subtracts the standard deviation from the average to obtain a difference as the threshold value.

Referring toFIG. 4, the monitoring procedure4of the method for monitoring a machine includes steps41-45.

In step41, the transducer11captures a sound produced during operation of the target machine10, and then converts the sound thus captured into a to-be-tested dataset. The transducer11then transmits the to-be-tested dataset to the processing unit13for the following analysis.

Upon receiving the to-be-tested dataset from the transducer11, in step42, the processing unit13first performs time-frequency analysis on the to-be-tested dataset to generate a to-be-tested spectrogram based on the to-be-tested dataset. In some embodiments, in order to reduce data values that are related to ambient noise in the to-be-tested dataset, the processing unit13may further perform exponentiation on the to-be-tested dataset before performing the time-frequency analysis on the to-be-tested dataset. Accordingly, data values that are related to a part of the sound having a relatively greater volume (e.g., greater than an average volume of the sound) will be increased, while data values that are related to a part of the sound having a relatively lower volume (e.g., lower than the average volume) will be decreased, which alleviates effects of ambient noise on a result of the time-frequency analysis. In some embodiments, the data values that are related to the part of the sound having a volume greater than the average volume are each multiplied by a value greater than one, while the data values that are related to the part of the sound having a volume lower than the average volume are each multiplied by a value smaller than one.

In step43, the processing unit13inputs the to-be-tested spectrogram to the analysis model to obtain an analysis result. Specifically, the output of the analysis model with the to-be-tested spectrogram serving as the input is a similarity index that indicates similarity between the to-be-tested spectrogram and the group of training spectrograms and that serves as the analysis result.

In step44, the processing unit13determines whether the target machine10is abnormal based on the analysis result. Specifically, the processing unit13compares the analysis result (i.e., the similarity index) to the threshold value, and determines that the target machine10is abnormal when the similarity index is less than the threshold value and determines that the target machine10is normal when otherwise. The flow goes to step45when it is determined that the target machine10is abnormal, and goes back to step41when otherwise.

In step45, the processing unit13outputs an abnormal signal indicating that the target machine10is abnormal. Specifically, the processing unit13may transmit the abnormal signal to the output unit14so as to control the output unit14to output a warning in a form of a text message displayed on a display device of the output unit14or a sound outputted by a speaker of the output unit14.

In summary, the system1and the method for monitoring a machine uses the transducer11to capture the sound produced by the target machine10and to convert the sound into the to-be-tested dataset, and then the processing unit13performs time-frequency analysis of the to-be-tested dataset to generate the to-be-tested spectrogram and inputs the to-be-tested spectrogram to the analysis model to obtain the analysis result (similarity index) which is then used to determine whether the target machine10is abnormal. By virtue of the system1and the method, an abnormal state of the target machine10can be detected without disassembling the target machine10. Further, the transducer11is capable of capturing and converting a sound having an audio frequency between 20 Hz and 48 kHz or above 48 kHz according to embodiments of this disclosure, and the analysis model can be used to analyze a spectrogram related to a sound having relatively higher audio frequency. Therefore, the system1and the method can accurately detect whether the target machine10has an abnormality that may produce a high-frequency sound which is not perceivable by humans.