LEADER 04183nam 2200841z- 450 001 9910566460803321 005 20231214133229.0 035 $a(CKB)5680000000037773 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81086 035 $a(EXLCZ)995680000000037773 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInformation Theory and Its Application in Machine Condition Monitoring 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (194 p.) 311 $a3-0365-3208-0 311 $a3-0365-3209-9 330 $aCondition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $afault detection 610 $adeep learning 610 $atransfer learning 610 $aanomaly detection 610 $abearing 610 $awind turbines 610 $amisalignment 610 $afault diagnosis 610 $ainformation fusion 610 $aimproved artificial bee colony algorithm 610 $aLSSVM 610 $aD-S evidence theory 610 $aoptimal bandwidth 610 $akernel density estimation 610 $aJS divergence 610 $adomain adaptation 610 $apartial transfer 610 $asubdomain 610 $arotating machinery 610 $agearbox 610 $asignal interception 610 $apeak extraction 610 $acubic spline interpolation envelope 610 $acombined fault diagnosis 610 $aempirical wavelet transform 610 $agrey wolf optimizer 610 $alow pass FIR filter 610 $asupport vector machine 610 $asatellite momentum wheel 610 $aHuffman-multi-scale entropy (HMSE) 610 $asupport vector machine (SVM) 610 $aadaptive particle swarm optimization (APSO) 610 $arail surface defect detection 610 $amachine vision 610 $aYOLOv4 610 $aMobileNetV3 610 $amulti-source heterogeneous fusion 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aLi$b Yongbo$4edt$01295517 702 $aGu$b Fengshou$4edt 702 $aLiang$b Xihui$4edt 702 $aLi$b Yongbo$4oth 702 $aGu$b Fengshou$4oth 702 $aLiang$b Xihui$4oth 906 $aBOOK 912 $a9910566460803321 996 $aInformation Theory and Its Application in Machine Condition Monitoring$93023565 997 $aUNINA