Abstract
With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines, an accelerometer can be attached on the spindle to collect the data from the detected vibration of the CNC. However, due to their cost, accelerometers have not been widely adopted for use with typical CNC machine tools. This study sought to develop an embedded miniature MEMS microphone array system (Radius 5.25 cm, 8 channels) to discover the vibration source of the CNC from spatial phase array processing. The proposed method utilizes voice activity detection (VAD) to distinguish between the presence and absence of abnormal noise in the pre-stage, and utilizes the traditional direction of arrival method (DOA) via multiple signal classification (MUSIC) to isolate the spatial orientation of the noise source in post-processing. In the numerical simulation, the non-interfering noise source location is calibrated in the anechoic chamber, and is tested with real milling processing in the milling machine. As this results in a high background noise level, the vibration sound source is more accurate in the presented energy gradation graphs as compared to the traditional MUSIC method.
Highlights
In recent years, industrial exhibitions have presented the latest measuring instruments and systems for Industry 4.0, including those in the area of electronic acoustics
File transfer protocol (FTP) wireless transmission was used to transfer the data to a personal computer (PC)
The signals of the microphone array were collected by Xilinx Field-Programmable Gate Array (FPGA), which allowed the data from the array to approach synchronous transmission
Summary
Industrial exhibitions have presented the latest measuring instruments and systems for Industry 4.0, including those in the area of electronic acoustics. Due to their wide variety of frequency responses and their use in huge environmental spaces, microphone arrays have continuously been applied in new ways, allowing for novel uses of a relatively old technology. The central processing unit (CPU) speed of electronic acoustic digital sensors was slow and inaccurate, while analog sensors, which required large and bulky equipment, were expensive. With the semiconductor industry booming in recent years, CPU processing has become cheaper and faster, allowing digital sensors to be modularized . DSP can be applied on the industry automation production line
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