Flush type bearing failure intelligent diagnosing device based on ARM (advanced RISC machine) and DSP (digital signal processor)

Abstract

The invention discloses a flush type bearing failure intelligent diagnosing device based on an ARM (advanced RISC machine) and a DSP (digital signal processor). The device comprises a signal input module connected with a vibration signal of a detected bearing, wherein the signal input module is connected with a core operation module, a serial communication module and a control module in sequence. A structure of DSP plus ARM is used in the invention, and an ARM sub-system and a DSP sub-system are communicated through an RS232 (recommend standard 232) serial bus; and under the system structure, real-time sampling and calculation as well as the management and control of a system can be executed in parallel, and the two sub-systems are synchronized on task execution by virtue of communication between the two sub-systems. The dual-CPU (central processing unit) structure-based system plays a critical role in protecting the real time property, and a multi-scale envelope spectrum analysis algorithm based on Morlet wavelet transformation is adopted in the core algorithm, and is realized in flush-type hardware for the first time, so that not only can the accuracy of bearing failure diagnosis be improved, but also the portability is realized.

Description

technical field [0001] The invention relates to a bearing fault intelligent diagnosis device based on DSP and ARM dual-core embedded technology. Background technique [0002] As we all know, the failure of rotating machinery caused by bearing failure is not uncommon (for example, in the study of motor reliability co-sponsored by General Electric and IEEE Industrial Application Society, it was found that the problems caused by bearings accounted for more than 40% of the total machine failure). Therefore, the development and research of various bearing fault diagnosis systems corresponding to this has attracted widespread attention from the industry and academia. The traditional bearing fault diagnosis system is based on classical Fourier analysis, which detects and diagnoses faults by performing Fourier transform on the measured signal to obtain characteristic spectra such as amplitude and phase. It reflects the presence or absence of bearing faults. Due to the transient an...

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Problems solved by technology

Due to the transient and non-stationary nature of the vibration signals generated during the formation and development of bearing faults, the analysis of such signals by Fourier transform is an averaging of the signals over the entire time period by Fourier transform. The frequency characteristic representation of the signal (that is, it looks at the spectral composition of the signal from a global perspective, and needs to assume that the signal is stationary on the entire time axis), it cannot reflect the spectral characteristics of the signal in the local time region, it is this defect Reflects the limitations of the Fourier transform

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