An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Chromatic monitoring of gear mechanical degradation based on acoustic emission

This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.Peer ReviewedPostprint (published version

Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

Distributed embedded system with internet GSM connectivity for intelligent e-monitoring of machine tools

Machining is one of the most important operations in many industrial environments. To prosper in today's competitive industrial world any machining system should be able to deliver the highest possible quality at the lowest possible costs, with very high reliability and flexibility. To fulfil these requirements the idea of e-Monitoring an industrial process was introduced by the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff University. It has considerable potential applications in industrial systems to not only monitor the health of the machines but also for data management and presentation for future decision making. The research presented in this thesis considers the evolution of two different low complexity signal analysis techniques which can be used for e-Monitoring the health of the cutters used in milling machine tools. The researched techniques are based in the time and frequency domains. The frequency domain analysis technique is based on the idea of using switched capacitor filters and microcontrollers to monitor the frequencies of interest in existing machine tool signals (spindle load and speed) thus avoiding the need for external sensors. The results of frequency domain analysis are used to assess the health of the cutter. The time domain analysis technique uses the same signals to analyse any variations within a tool rotation period and relate these to the health of the cutter. The results are integrated before final decision making which helps in reducing false alarms. The thesis goes on to logically describe the design and development of an on-line microcontroller based distributed intelligent e-Monitoring system for a milling machine tool model Kondia B500, using the proposed signal analysis techniques. Some additional features such as internet and GSM connectivity have also been added to the designed system. The designed system was interfaced to the machine tool and tested for its reliability which was found to be competitive with many other very expensive systems. The designed system can be fitted into a machine tool at the manufacturing stage or it could be interfaced to an existing machine tool for automatically detecting a tooth breakage

Experiment and Simulation of the Acoustic Signature of Fatigued-Cracked Gears in a Two-Stage Gearbox

This thesis focuses on the development of a health monitoring system for gearbox transmissions. This was accomplished by developing and understanding a two-stage gearbox computer model that emulates an actual gearbox test rig. The computer model contains actual gearbox geometry, flexible shafts, bearings, gear contact forces, input motor torque, output brake torque, and realistic gearbox imbalance. The gear contact force of each gear stage and the input bearing translational acceleration were the main outputs compared between a healthy gearbox and damaged gearbox computer model. The damage of focus was a fatigue crack on the input pinion gear. A sideband energy ratio comparison yielded the computer simulation accurately modeled the difference between a healthy and damaged gearbox. The next step in this study involved the development of a repeatable procedure to initiate and propagate a fatigue crack at the tooth root in an actual spur gear. A damaged spur gear allows for a future comparison of an actual healthy and damaged gearbox system in the lab. A custom fatigue fixture was designed and manufactured for a Martin S1224BS 1 spur gear. The fatigue crack was initiated by position control fatigue testing which deflects the gear tooth a set amplitude for a number of cycles. Over the length of the test, the load that the tooth can withstand in bending decreases as damage begins to occur. Once the max load on the gear has dropped by a significant percentage (5-15%) a crack has initiated and begun to propagate across the tooth face. The use of a scanning electron microscope confirmed the presence a fatigue crack

Wireless Sensor Integrated Tool for Characterization of Machining Dynamics in Milling

A first step towards practical sensing in the machining environment is the development and use of low cost, reliable sensors. Historically, the ability to record in-process data at an end mill tool tip has been limited by the sensor location. Often, these sensors are mounted on the material workpiece or the machine spindle at significant physical distance from the cutting process. Of specific interest are the problems of tool chatter which causes limitations to productivity and part quality. Although tool chatter is a substantial issue in machining, it remains an open research topic. In this research, a sensor integrated cutting tool holder is developed to specifically analyze the problems related to tool chatter. With the sensor integrated cutting tool holder, the signal to noise ratio is higher than traditional sensing methods. Because of the higher sensitivity, new data analysis methods can be explored. Specifically, the sensor is used in conjunction with a data dependent linear predictive coding algorithm to demonstrate effective prediction of chatter frequencies from stable cutting

Fault Detection in Rotating Machinery: Vibration analysis and numerical modeling

This thesis investigates vibration based machine condition monitoring and consists of two parts: bearing fault diagnosis and planetary gearbox modeling. In the first part, a new rolling element bearing diagnosis technique is introduced. Envelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding the suitable frequency band for demodulation has been a substantial challenge for a long time. Introduction of the Spectral Kurtosis (SK) and Kurtogram mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise these methods will fail. This major drawback may noticeably decrease their effectiveness and goal of this thesis is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of 2nd order cyclostationarity, especially in the presence of localized faults. A second-order cyclostationary signal is one whose autocovariance function is a periodic function of time: the proposed method, named Autogram by the authors, takes advantage of this property to enhance the conventional Kurtogram. The method computes the kurtosis of the unbiased autocorrelation (AC) of the squared envelope of the demodulated and undecimated signal, rather than the kurtosis of the filtered time signal. Moreover, to take advantage of unique features of the lower and upper portions of the AC, two modified forms of kurtosis are introduced and the resulting colormaps are called Upper and Lower Autogram. In addition, a new thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. Finally, the proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics. Moreover, a second novel method for diagnosis of rolling element bearings is developed. This approach is a generalized version of the cepstrum pre-whitening (CPW) which is a simple and effective technique for bearing diagnosis. The superior performance of the proposed method has been shown on two real case data. For the first case, the method successfully extracts bearing characteristic frequencies related to two defected bearings from the acquired signal. Moreover, the defect frequency was highlighted in case two, even in presence of strong electromagnetic interference (EMI). The second part presents a newly developed lumped parameter model (LPM) of a planetary gear. Planets bearings of planetary gear sets exhibit high rate of failure; detection of these faults which may result in catastrophic breakdowns have always been challenging. Another objective of this thesis is to investigate the planetary gears vibration properties in healthy and faulty conditions. To seek this goal a previously proposed lumped parameter model (LPM) of planetary gear trains is integrated with a more comprehensive bearing model. This modified LPM includes time varying gear mesh and bearing stiffness and also nonlinear bearing stiffness due to the assumption of Hertzian contact between the rollers/balls and races. The proposed model is completely general and accepts any inner/outer race bearing defect location and profile in addition to its original capacity of modelling cracks and spalls of gears; therefore, various combinations of gears and bearing defects are also applicable. The model is exploited to attain the dynamic response of the system in order to identify and analyze localized faults signatures for inner and outer races as well as rolling elements of planets bearings. Moreover, bearing defect frequencies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, frequency response of the system for different sizes of planets bearing faults are compared and statistical diagnostic algorithms are tested to investigate faults presence and growth

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201