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

Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

The detection, diagnostic, and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines, especially in key industrial sectors. This paper presents a new approach that combines the Hilbert-Huang transform (HHT), the support vector machine (SVM), and the support vector regression (SVR) for the monitoring of ball bearings. The proposed approach uses the HHT to extract new heath indicators from stationary/nonstationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called SVM, and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time-series prediction based on SVR. A set of experimental data collected from degraded bearings is used to validate the proposed approach. The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.

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Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.

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Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Generalized roughness is the most common damage occurring to rolling bearings. It produces a frequency spreading of the characteristic fault frequencies, thus making it difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is proposed here in order to identify the spreading bandwidth related to specific conditions, relying on current or vibration measurements only. Then, a diagnostic index based on the computation of the energy in the previously defined bandwidth is used to diagnose bearing faults. The proposed method was validated experimentally with vibration signals, with robust and reliable results. The same procedure can be extended to current signals.

Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals

This paper investigates the relationship between vibration and current in induction motors operated under external vibrations Two approaches are usually available to define this relationship The former is based on airgap variations, while the latter is based on torque perturbation This paper is focused on the airgap variation model The ball bearing fault is modeled by contact mechanics External vibrations often occur in many industrial applications where externally induced vibrations of suitable amplitude cause cyclic radial loading on the machine shaft The model is validated by experiments, owing to a dedicated test setup, where an external vibration source (shaker) was employed, together with ball bearing alterations in order to decrease the stiffness of the support along the radial direction To maximize the effects of externally induced vibrations, the frequency chosen was near the flexural resonance of the rotor (determined by finite-element method analysis) The direction of the external vibration is radial with respect to the axis of the electric machine under test During tests, both stator phase currents and vibration of the machine were sampled The test setup allowed one to vary the machine speed and load, vibration amplitude, and bearing stiffness (damage level) Radial effects are usually visible only in the case of large failures that result in significant airgap variations, as confirmed by experiments

Bearing Fault Model for Induction Motor With Externally Induced Vibration

Electric linear motors are spreading in industrial automation because they allow for direct drive applications with very high dynamic performances, high reliability, and high flexibility in trajectory generation. The moving part of the motor is linked to the fixed part by means of linear bearings. As in many other electric machines, bearings represent one of the most vulnerable parts because they are prone to wear and contamination. In the case of linear roller bearings, this issue is even more critical as the rail cannot be easily fully enclosed and protected from environmental contamination, unlike the radial rotating bearing counterpart. This paper presents a diagnostic method based on vibration analysis to identify which signature is related to a specific fault.

Fault Detection of Linear Bearings in Brushless AC Linear Motors by Vibration Analysis

This paper focuses on the diagnostics of ball bearings in direct-drive motors These specific AC brushless motors are increasing their importance in automation machineries because they can work with a built-in flexibility In particular the angular displacement of the shaft is continuously monitored by an embedded encoder while the control system allows to perform complex motion profiles such as polynomial ones, even with the inversion of the rotating direction Direct-drive motors avoid the presence of a mechanical cams or gearboxes between the motor and the load with a subsequent money-saving On the other side, unfortunately, the diagnostics of ball bearing in those motors is not trivial In fact most of the solutions proposed in the literature require a constant frequency rotation of the shaft since the characteristic fault frequencies are directly proportional to speed of the motor It follows that in a varying speed application the fault characteristic frequencies change instantaneously as the rotational frequency does In this paper an industrial application is considered, where the direct drive motors are used in the kinematic chain of an automated packaging machine performing a cyclic polynomial profile The basic idea is to focus on signal segmentation using the position profile of the shaft – directly measured by the encoder – as trigger Next the single cycles of the machine is analysed in time domain, again using encoder signal machine contribution is deleted Feature extraction for damage detection is done by applying the Short Time Fourier Transform (STFT), the STFT for each cycle is averaged in time-frequency domain in order to enhance fault signature Finally, the sum of STFT coefficients is used as a simple indicators of damage

Marco Cocconcelli

Papers

Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals

Bearing Fault Model for Induction Motor With Externally Induced Vibration

Fault Detection of Linear Bearings in Brushless AC Linear Motors by Vibration Analysis

STFT Based Approach for Ball Bearing Fault Detection in a Varying Speed Motor

An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles