C. K. Madhusudana¹, N. Gangadhar¹, Hemantha Kumar, Kumar,^*,1, S. Narendranath¹

Structural Durability & Health Monitoring, Vol.12, No.2, pp. 111-127, 2018, DOI:10.3970/sdhm.2018.01262

Abstract This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool More >

T. M. Alamelu Manghai¹, R. Jegadeeshwaran²

Structural Durability & Health Monitoring, Vol.11, No.2, pp. 149-167, 2017, DOI:10.3970/sdhm.2017.011.149

Abstract Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road. Therefore, monitoring the condition of the brake components is inevitable. The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches. The vibration signals were captured using an accelerometer sensor under a various fault condition. The acquired vibration signals were processed for extracting meaningful information as features. The condition of the brake system can More >

Rahul Kumar Sharma^*1, V. Sugumaran¹, Hemantha Kumar², Amarnath M³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone)… More >

R. Jegadeeshwaran¹, V. Sugumaran², K.P. Soman³

Structural Durability & Health Monitoring, Vol.10, No.1, pp. 81-97, 2014, DOI:10.3970/sdhm.2014.010.081

Abstract In automobile, brake system is an essential part responsible for control of the vehicle. Vibration signals of a rotating machine contain the dynamic information about its health condition. Many research papers have reported the suitability of vibration signals for fault diagnosis applications. Many of them are based on (Fast Fourier Transform) FFT, which have their own drawback with nonstationary signals. Hence, there is a need for development of new methodologies to infer diagnostic information from such non stationary signals. This paper uses vibration signals acquired from a hydraulic brake system under good and simulated faulty… More >

A.S. Koyuncugil¹, N. Ozgulbas²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.2, pp. 39-46, 2009, DOI:10.3970/icces.2009.011.039

Abstract In this paper, a data mining model for detecting financial and operational risk indicators by CHAID Decision Tree is presenting. The identification of the risk factors by clarifying the relationship between the variables defines the discovery of knowledge from the financial and operational variables. Automatic and estimation oriented information discovery process coincides the definition of data mining. During the formation of model; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the More >