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This paper proposes an intelligent monitoring method for power machinery failures based on acoustic wave characteristics, which involves the construction of an improvedconvolutional neural network (CNN) and long short-term memory (LSTM) model. From an implementation perspective, the proposed method consists of three key steps: the development of an intrinsic acoustic wave database related to a mechanical equipment failure, the execution of intelligent failure monitoring, and the creation of a visual operation interface. Specifically, when a mechanical failure occurs, acoustic wave data containing information about the failure is collected and processed. The fast Fourier transform (FFT) is employed to extract the acoustic wave characteristics associated with equipment failures. A comprehensive database of these characteristics is compiled from various equipment failures, such as fan blade damage and pump body leakage, and stored as a failure eigen acoustic wave database. Then, during the intelligent monitoring of specific mechanical equipment failures, the intrinsic acoustic wave database acquired from these failures serves as an embedded feature for extracting and outputting the corresponding failure's acoustic wave fragments. This process enables the identification of the failure type and facilitates accurate early warning regarding potential equipment failures. Furthermore, a visual operational interface has been developed based on an improved CNN-LSTM neural network model, which allows for straightforward and precise intelligent monitoring of mechanical failures. This intelligent monitoring approach, grounded in the characteristics of acoustic waves, offers several advantages, including low cost, ease of deployment, and high identification efficiency. These attributes make it particularly well-suited for applications in complex operational environments within power machinery systems across various industries, including aerospace and nuclear power.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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