Abstract
High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.
Highlights
Condition monitoring is a systematic project which includes a series of processes: data acquisition, transmission, storage, processing, condition recognition, and visualization
The main contributions of this paper include: perceptual hashing is formally introduced as an edge computing form for machine condition information extraction and representation, which leads to a new framework for diagnosis and prognosis; a sub-band coding method is developed for perceptual vibration hashing, in which conventional wavelet packet transform (WPT) and two-dimensional discrete cosine transform (2D-DCT) are adopted for sub-band division and feature extraction, the aggregate approximation (SAX) can compactly represent the sub-band feature in a symbolic way, condition monitoring efficiency can be improved; a new form of machine condition hash is defined as the information carrier exchanged between the monitoring terminal and the server, with which the condition inference on the server can be decoupled from the machine condition hash generation on the terminal
The same rationale of sub-band coding is followed in this paper to extract, quantize, and represent the machine condition information, whereas the perceptual criteria are not determined by the human sensory organs but the three computing characteristics listed in Section II– compactness, robustness, and discriminability
Summary
Condition monitoring is a systematic project which includes a series of processes: data acquisition, transmission, storage, processing, condition recognition, and visualization. The main contributions of this paper include: perceptual hashing is formally introduced as an edge computing form for machine condition information extraction and representation, which leads to a new framework for diagnosis and prognosis; a sub-band coding method is developed for perceptual vibration hashing, in which conventional wavelet packet transform (WPT) and two-dimensional discrete cosine transform (2D-DCT) are adopted for sub-band division and feature extraction, the aggregate approximation (SAX) can compactly represent the sub-band feature in a symbolic way, condition monitoring efficiency can be improved; a new form of machine condition hash is defined as the information carrier exchanged between the monitoring terminal and the server, with which the condition inference on the server can be decoupled from the machine condition hash generation on the terminal. The same rationale of sub-band coding is followed in this paper to extract, quantize, and represent the machine condition information, whereas the perceptual criteria are not determined by the human sensory organs but the three computing characteristics listed in Section II– compactness, robustness, and discriminability.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call