Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To diagnosis the fault of automaton using vibration signals, a method based on local narrow-band decomposition and frequency-domain fuzzy nearness degree was proposed. Three different faults re set on easy-to-crack locations of automaton, using electrospark wire-electrode cutting. Then two groups of piezoelectrical acceleration transducers re set on the front of the catridge receiver and the top of the machine gun's tail respectively. Each transducer group detected the vibration of two directions. The author took one set of signals (five shoots in succession) from each working condition's database as a test sample. And another sample (three shoots in succession) from Fault 3 signal database was taken additionally as the reference sample. In order to diagnose the fault more easily, the local narrow-band decomposition was used to highlight fault features. Then the similarity measurement based on frequency-domain fuzzy nearness degree was used to compare similarities beten the reference sample and each test sample. The results shod that the nearness degree of Fault 3 (five shoots in succession) was much lor than other test samples. So this method could distinguish different working conditions of automaton and reflect the severity of fault.
['Mingzhi Pan', 'Yuan Tian', 'Hongxia Pan', 'Xu Xin']
I am writing a research paper on a idea Method of automaton fault diagnosis based on narrow-band decomposition and fuzzy nearness degree
[{"from": "human", "value": "I am writing a research paper on a idea Method of automaton fault diagnosis based on narrow-band decomposition and fuzzy nearness degree"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To diagnosis the fault of automaton using vibration signals, a method based on local narrow-band decomposition and frequency-domain fuzzy nearness degree was proposed. Three different faults re set on easy-to-crack locations of automaton, using electrospark wire-electrode cutting. Then two groups of piezoelectrical acceleration transducers re set on the front of the catridge receiver and the top of the machine gun's tail respectively. Each transducer group detected the vibration of two directions. The author took one set of signals (five shoots in succession) from each working condition's database as a test sample. And another sample (three shoots in succession) from Fault 3 signal database was taken additionally as the reference sample. In order to diagnose the fault more easily, the local narrow-band decomposition was used to highlight fault features. Then the similarity measurement based on frequency-domain fuzzy nearness degree was used to compare similarities beten the reference sample and each test sample. The results shod that the nearness degree of Fault 3 (five shoots in succession) was much lor than other test samples. So this method could distinguish different working conditions of automaton and reflect the severity of fault."}]