TY - JOUR
T1 - Machinery fault diagnosis and prognosis
T2 - application of advanced signal processing techniques
AU - Suh, Jae Hong
AU - Kumara, Soundar R.T.
AU - Mysore, Shreesh P.
N1 - Funding Information:
The authors acknowledge the partial support of ONR Multi University Research Initiative (MURI) grant N00014-95-1-0461, on integrated predictive diagnostics.
PY - 1999
Y1 - 1999
N2 - Machinery health prediction based on process models and/or process parameters is an important function in automated manufacturing set-ups. Sensor data is collected and used to indirectly model the equipment. Due to the short response times required it is important to investigate robust sensor data representation schemes. Traditional Fourier Analysis is not sufficient to preserve the information in both frequency and time domains. In this paper we describe the use of wavelets, both continuous and discrete, for equipment diagnosis and prognosis. We detail wavelet-based techniques for gear fault diagnosis and prognosis.
AB - Machinery health prediction based on process models and/or process parameters is an important function in automated manufacturing set-ups. Sensor data is collected and used to indirectly model the equipment. Due to the short response times required it is important to investigate robust sensor data representation schemes. Traditional Fourier Analysis is not sufficient to preserve the information in both frequency and time domains. In this paper we describe the use of wavelets, both continuous and discrete, for equipment diagnosis and prognosis. We detail wavelet-based techniques for gear fault diagnosis and prognosis.
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U2 - 10.1016/S0007-8506(07)63192-8
DO - 10.1016/S0007-8506(07)63192-8
M3 - Article
AN - SCOPUS:0032642515
SN - 0007-8506
VL - 48
SP - 317
EP - 320
JO - CIRP Annals - Manufacturing Technology
JF - CIRP Annals - Manufacturing Technology
IS - 1
ER -