THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Applications of machine learning to machine fault diagnosis: A review and roadmap

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Metal Cutting Principles

There is a constant call for reduction of operational and maintenance costs of induction motors (IMs) These costs can be significantly reduced if the health of the system is monitored regularly This allows for early detection of the degeneration of the motor health, alleviating a proactive response, minimizing unscheduled downtime, and unexpected breakdowns The condition based monitoring has become an important task for engineers and researchers mainly in industrial applications such as railways, oil extracting mills, industrial drives, agriculture, mining industry etc Owing to the demand and influence of condition monitoring and fault diagnosis in IMs and keeping in mind the prerequisite for future research, this paper presents the state of the art review describing different type of IM faults and their diagnostic schemes Several monitoring techniques available for fault diagnosis of IM have been identified and represented The utilization of non-invasive techniques for data acquisition in automatic timely scheduling of the maintenance and predicting failure aspects of dynamic machines holds a great scope in future

Condition Monitoring and Fault Diagnosis of Induction Motors: A Review

This review focuses on the effect of metal-containing nanomaterials on tribological performance in oil lubrication. The basic data on nanolubricants based on nanoparticles of metals, metal oxides, metal sulfides, nanocomposities, and rare-earth compounds are generalized. The influence of nanoparticle size, morphology, surface functionalization, and concentration on friction and wear is analyzed. The lubrication mechanisms of nanolubricants are discussed. The problems and prospects for the development of metal-containing nanomaterials as lubricant additives are considered. The bibliography includes articles published during the last five years.

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Metal-containing nanomaterials as lubricant additives: State-of-the-art and future development

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines.

Support vector machines based non-contact fault diagnosis system for bearings

Condition monitoring of gearboxes which is considered as a key element of rotating machines ensures to continuously reduce and eliminate cost, unscheduled downtime and unexpected breakdowns. Although, a lot of work on condition monitoring and fault diagnosis of fixed-axis gearbox has been reported in the literature, however only a few have found their way to industrial applications. The ability of condition statistical indicators is to provide accurate and precise information about the health of various components at different levels of damage. In this paper, frequently used condition indicators are addressed domain-wise and their characteristics are stated. This paper presents the review of work to provide a wide and good reference for researchers to be utilized. The structure of a fixed-axis gearbox in addition to the unique behaviors and fault characteristics of fixed-axis gearbox has been recognized and represented. By extensively reviewing and categorizing important papers and articles, this paper is able to summarize the conditional monitoring indicators on basis of adopted methodologies. Lastly, open problems are stated and further research prospects pointed out.

Condition Monitoring Parameters for Fault Diagnosis of Fixed Axis Gearbox: A Review

Tool vibration and surface roughness are two important parameters which affect the quality of the component and tool life which indirectly affect the component cost. In this paper, the effect of cutting parameters on tool vibration, and surface roughness has been investigated during end milling of EN-31 tool steel. Response surface methodology (RSM) has been used to develop mathematical model for predicting surface finish, tool vibration and tool wear with different combinations of cutting parameters. The experimental results show that feed rate is the most dominating parameter affecting surface finish, whereas cutting speed is the major factor effecting tool vibration. The results of mathematical model are in agreement with experimental investigations done to validate the mathematical model.

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Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation

Machines without vibrations during operation are something non-existent which results in deterioration of machine tools. The condition based monitoring has become a crucial technique to ensure the machine availability by timely maintenance actions and reducing breakdown maintenance. Several condition monitoring techniques have been reported viz. vibration monitoring, acoustic emission monitoring, chemical monitoring, thermal monitoring and therefore new methods to improve the reliability, effectiveness, accuracy of the fault detection need to be evaluated. Vibration analysis is proven to be an important criterion for fault diagnosis in manufacturing processes and maintenance scheduling for various manufacturing equipment. This paper presents an overview of recent trends in condition monitoring and signal processing methods used for predictive maintenance of dynamic systems.

Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniques — A review

Timely detection of faults in bearings can save time, efforts and maintenance costs of rotating equipments. To avoid the physical connection of vibration pickup to the machine tool, a non-contact type vibration pickup has been designed and developed in this study to acquire the vibration data for bearing health monitoring under load and speed variation. Fault diagnosis has been accomplished using a Hilbert transform for denoising the signal. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the number of input features and finding the most optimal feature set. Finally, these selected features have been passed to Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for identifying and further classifying the various bearing defects. A comparative analysis of the effectiveness of SVM and ANN has been carried out. The results reveal that the vibration signatures obtained from developed non-contact sensor (NCS) compare well with the accelerometer data obtained under the same conditions. Classification accuracy achieved by the developed NCS with other sensors reported in the literature compares very well. The proposed strategy can be used for automatic recognition of machine faults which will help in providing early warnings to avoid unwanted and unplanned system shutdowns due to failure of the bearings.

Sukhdeep S. Dhami

Papers

Support vector machines based non-contact fault diagnosis system for bearings

Condition Monitoring Parameters for Fault Diagnosis of Fixed Axis Gearbox: A Review

Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation

Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniques — A review

Non-Contact Fault Diagnosis of Bearings in Machine Learning Environment