Vibroacoustic Condition Monitoring of the Complex Rotation System Based on Multilevel Signal Processing

Advanced modulation formats for optical access networks

From error probability to information theoretic signal and image processing

Error free recovery of signals from irregularly spaced samples in terms of completeness of sets of nonharmonic exponentials

This chapter deals with the impact of condition monitoring in mechanical system. Over the past few decades condition monitoring of the mechanical system is most interesting area for researchers. The extensively used mechanical equipments in severe conditions are subjected to failure and progressive deteriorations from their initial condition. For this suitable parameter has to be selected that indicates real-time internal conditions and fault incidence of the mechanical system. Therefore, to early detection of failure that may cause system shutdown, continuous monitoring of the system is more important aspect that reduces downtime of the systems and it also saves money. In mechanical system fault occurs due to imbalance of forces, fault in gearing system, bearing fault, loading conditions on shaft, and improper machine selection for particular work. To detect the fault at early stage, the system parameters like performance, vibrations, noise, temperature, pressure, wear at movable, and lubrication monitoring are required. In this regard, for monitoring the conditions of system parameter several sensors have been used to detect real-time conditions. The sensors signal is acquired in terms of electrical signal which has been processed by signal processing techniques and analyzed. In-process sensing and control of parameters are required to smooth operation of system. The use and benefits of sensor/transducers and advanced signal processing are having their own strengths and limitations. This chapter is intended to provide fundamentals of various condition monitoring techniques as well as signal processing methods with application in mechanical system. It also describes the various transducers used to provide the corresponding signals for condition monitoring, has been discussed. This chapter also intended that applications of condition monitoring in various mechanical system like identification of bearing defects, Vibration monitoring of rotary machine, cutting tool monitoring, machine tool monitoring, power plant monitoring, vehicles, guiding rails, 3D printing, identification of gears defects, lubricant condition monitoring, railway crossing, wind turbines, surface texture condition monitoring, grinding wheel condition monitoring, etc.

Reliable condition monitoring (CM) can be an effective means to significantly reduce wind turbine (WT) downtime, operations and maintenance costs and plan preventative maintenance in advance. The WT generator voltage and current output, if sampled at a sufficiently high rate (kHz range), can provide a rich source of data for CM. However, the electrical output of the WT generator is frequently shown to be complex and noisy in nature due to the varying and turbulent nature of the wind. Thus, a fully satisfactory technique that is capable to provide accurate interpretation of the WT electrical output has not been achieved to date. The objective of the research described in this thesis is to develop reliable WT CM using advanced signal processing techniques so that fast analysis of non-stationary current measurements with high diagnostic accuracy is achieved. The diagnostic accuracy and reliability of the proposed techniques have been evaluated using data from a laboratory test rig where experiments are performed under two levels of rotor electrical asymmetry faults. The experimental test rig was run under fixed and variable speed driving conditions to investigate the kind of results expected under such conditions. An effective extended Kalman filter (EKF) based method is proposed to iteratively track the characteristic fault frequencies in WT CM signals as the WT speed varies. The EKF performance was compared with some of the leading WT CM techniques to establish its pros and cons. The reported experimental findings demonstrate clear and significant gains in both the computational efficiency and the diagnostic accuracy using the proposed technique. In addition, a novel frequency tracking technique is proposed in this thesis to analyse the non-stationary current signals by improving the capability of a continuous wavelet transform (CWT). Simulations and experiments have been performed to verify the proposed method for detecting early abnormalities in WT generators. The improved CWT is finally applied for developing a new real-time CM technique dedicated to detect early abnormalities in a commercial WT. The results presented highlight the advantages of the improved CWT over the conventional CWT to identify frequency components of interest and cope with the non-linear and non-stationary fault features in the current signal, and go on to indicate its potential and suitability for WT CM.

Condition monitoring (CM) has been recognized as one of the most promising and widely applicable approaches to increase the availability and reduce the operation and maintenance (O&M) costs of wind turbines (WTs). However, up to date the potential of CM in the wind industry has not been fully exploited due to manifold reasons including (1) the lack of cost-effective and universal strategies that can deal with the CM issues from various concepts of WTs and (2) the lack of efficient and robust algorithms that can accurately process and interpret CM signals collected from WTs. In addition, WTs are subject to constantly varying loads due to inconsistent wind. In order to mitigate the complex loads and maximize the output power, modern megawatt-scale WTs are designed as variable-speed and pitch-control machines. Consequently, the CM signals collected from large scale WTs are usually non-stationary over time and difficult to process accurately using conventional signal processing methods. Thus, there is an urgent need of advanced CM strategies and dedicated signal processing techniques for WTs. Here a novel WT drivetrain CM strategy and the associated signal processing method, namely, wavelet-transform-based energy tracking technique (WETT), are elaborated in this chapter. The WETT utilizes readily available generator power signal to evaluate the health condition of the whole WT drivetrain system through extracting and assessing the energy of WT power signals at fault characteristic frequencies. The WETT is verified through applying it to detecting the electrical and mechanical faults emulated on a WT drivetrain test rig. Experiment has shown that the WETT can correctly identify the simulated faults and is therefore potentially a successful tool for WT drivetrain CM.

Among the detection processes that have recently been described for use in a 9600 bit/s modem are `pseudobinary? processes that are developments of the pseudobinary Viterbi detector. Although no information has been published on the latter, it is of considerable interest both in its own right and in its performance relative to that of other detectors. The aim of the paper is to correct this omission. The pseudobinary Viterbi detector effectively recodes the received multilevel data signal into a binary signal having the same element (symbol) rate as the original signal and implements the conventional Viterbi-algorithm detection process for the corresponding binary signal, thus verygrcatly reducting the complexity of the detector. The paper considers this detector and also a basically different technique, that is a development of a conventional nonlinear qualiser. The two systems may be used with multilevel signals in many different applications and are studied here in an application of digital data transmission at 9600 bit/s over the pulic switched telephone network, where the transmitted signal is a 16-level QAM signal. The particular development of the nonlinear equaliser studied here has not previously been tested under these conditions, and it has furthermore been found to have an interesting relationship with the pseudobinary Viterbi detector. After describing the two detection processes, the paper presents the results of computer-simulation tests over models of two different telephone circuits, comparing the tolerances to additive white Gaussian noise of various arrangements of each process.

Condition monitoring provides important measurement data for process and production control. The overall system is formed as a continuum of consistent systems. Stabilising control needs continuous condition monitoring where the real-time stress and condition indicators are obtained by using signal processing and feature extraction. Signal processing is needed for waveform signals. The nonlinear scaling brings the severity level to the intelligent indicators which are used in the same way as process measurements. Periodic condition monitoring indicators are based on samples which are analysed in the same way as the continuous data. These indicators are for optimising and coordinating control and may need diagnostics and prognostics. Many methodologies can be utilised within a time range which can be suitable for using performance indicators as well. This level can be utilised in adaptation of control strategies, e.g.for adjusting the maintenance timing. These considerations are understood as performance monitoring which is a part of asset management. The condition monitoring is an important part of the unified solution of all this. Uncertainties and time windows are increasing when going from stabilized control to production control, maintenance and technical asset management. Since a huge number of equipment is waiting to be analysed, the automatic solutions should be extended within these areas.

Improper channelisation of kinetic energy induces vibrations in machinery. Vibration of mechanical equipment is generally not preferred. Hence, the source of vibration that will lead to failure need to be identified well in advance and corrected to avoid down time of the machine. Vibration has become a scientific tool for fault diagnosis. This paper emphasises monitoring the condition of a primary fan located at a large utility thermal power plant, consisting of 8 units and each, in turn is generating 210 MW. Tri-axial measurements are taken during the period of investigation. Regular logging of the data has provided a basis for performance trend monitoring. Fault diagnosis has been taken-up using ISO 2373 Standards. At the end of the investigation, remedial measures are suggested to bring down the offensive vibration. 1.INTRODUCTION :- Condition Monitoring is the analysis and interpretation of signals sensed by the transducers installed on operational machinery to assess their health. The analysis of the information provided by the transducers is done by using established techniques and interpretation of the evaluated output is considered further to establish what actions are to be taken. Condition monitoring also known as Predictive Maintenance is the technique of monitoring the operating characteristics of the equipment in such a manner that changes in monitored characteristics can be used to predict the need for maintenance well in advance before a serious deterioration or breakdown occurs. It is a quality assurance program for continuous process which inturn increases the production rate reducing downtime of the equipment. Condition monitoring can also be a test and quality assurance system for continuous processes as well as discrete component manufacture. It maximizes the performance of the company's assets by monitoring their condition and ensuring that they are installed and maintained correctly. The technique aims at detecting condition leading to catastrophic breakdown and loss of service, reducing maintenance overhauls, fine tuning of operating equipment, increasing production and operating efficiency and minimizing the replacement parts inventory. This is because a readily monitorable parameter of deterioration can be found in every plant machinery and probabilistic failure of an element in future is highly reduced or almost eliminated thus maximizing the item's life by minimizing the effect of failure. The dynamic behaviour of shafts rotating in bearings has attracted an enormous amount of research interest in the past three decades and have addressed such problems as critical speeds, rotor stability and its responses, nonlinear behaviour of the lubricating oil film etc., There is a growing awareness within production and maintenance circles of the benefits to be enjoyed from vibration based condition monitoring applied to rotating shafts. To increase reliability and accuracy of the monitoring techniques, the parameter identification methods offer potential benefits. The methods have been successfully used in active and adaptive controls of complex machinery. Due to demands of high-speed operation and the use of light structures in primary fans, dynamic measurements are necessary and vibration testing has therefore found wide spread use. The basis for such a health monitoring is to make some relevant calculations together with practical measurements. Primary fan rotors are of steel and stiff. However stiff they deflect by a small amount. This distortion creates 1

Non-invasive fetal electrocardiogram : analysis and interpretation

The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically reach, and generate more revenue than previous models. This paper reviews challenges and candidate technologies for next generation condition monitoring in Wind Energy.
 Larger wind turbines typically employ Doubly-Fed induction generators with gearbox based drive trains or direct drive generators with multi pole rotors and fixed stators. Both configurations employ variable speed wind driven rotors, variable due to wind speed. Fixed rotor speed signal processing techniques no longer work in a variable speed environment. Synchronous sampling, order analysis, wavelet filters, Cepstrum and related frequency analysis of sensor waveforms are examples of advanced feature extraction tools now available for up-tower condition monitoring systems to address the variable speed nature of modern wind turbines. These signal processing tools operate to reduce and preprocess sensory data producing and extracting signal features. With extracted features, performance prediction and health diagnostics are then able to produce machine degradation rate and degradation levels.
 This paper provides a tutorial of signal processing techniques for analysis of sensory information from variable speed rotary machines. The paper concludes with a discussion of prediction and diagnostics techniques which consume the analysis results of previously mentioned signal processing techniques.

Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational machinery, with a particular focus on bearing health. This paper provides a comprehensive literature review of recent advancements in intelligent condition monitoring technologies for rolling element bearings. Fundamental monitoring strategies are introduced, covering various sensing, signal processing, and feature extraction techniques for detecting defects in rolling element bearings. While vibration-based monitoring remains prevalent, alternative sensor types are also explored, offering complementary diagnostic capabilities or detecting different defect types compared to accelerometers alone. Signal processing and feature extraction techniques, including time domain, frequency domain, and time–frequency domain analysis, are discussed for their ability to provide diverse perspectives for signal representation, revealing unique insights relevant to condition monitoring. Special attention is given to information fusion methodologies and the application of intelligent algorithms. Multisensor systems, whether homogeneous or heterogeneous, integrated with information fusion techniques hold promise in enhancing accuracy and reliability by overcoming limitations associated with single-sensor monitoring. Furthermore, the adoption of AI techniques, such as machine learning, metaheuristic optimisation, and deep-learning methods, has led to significant advancements in condition monitoring, yielding successful outcomes with improved accuracy and robustness in various studies. Finally, avenues for further advancements to improve monitoring accuracy and reliability are identified, offering insights into future research directions.

Single-Mode Fibers for High Speed and Long-Haul Transmission Multimode Fibers for Data Centers Multi-core Fibers for Space Division Multiplexing Optical Coherent Detection and Digital Signal Processing of Channel Impairments A Brief History of Fiber-Optic Soliton Transmission Perturbations of Solitons in Optical Fibers Emission of Dispersive Waves from Solitons in Axially Varying Optical Fibers Nonlinear Waves in Multimode Fibers Shock Waves A Variety of Dynamical Settings in Dual-Core Nonlinear Fibers Advanced Nano-engineered Glass-Based Optical Fibers for Photonics Applications Fabrication of Negative Curvature Hollow Core Fiber Optimized Fabrication of Thulium Doped Silica Optical Fiber Using MCVD Microfiber: Physics and Fabrication Flat Fibers: Fabrication and Modal Characterization 3D Silica Lithography for Future Optical Fiber Fabrication Rare-Earth-Doped Laser Fiber Fabrication Using Vapor Deposition Technique Powder Process for Fabrication of Rare Earth-Doped Fibers for Lasers and Amplifiers Progress in Mid-infrared Fiber Source Development Crystalline Fibers for Fiber Lasers and Amplifiers Cladding-Pumped Multicore Fiber Amplifier for Space Division Multiplexing Optical Amplifiers for Mode Division Multiplexing Optical Fibers for High-Power Lasers Multicore Fibers Polymer Optical Fibers Optical Fibers in Terahertz Domain Optical Fibers for Biomedical Applications Basics of Optical Fiber Measurements Measurement of Active Optical Fibers Characterization of Specialty Fibers Characterization of Distributed Birefringence in Optical Fibers Characterization of Distributed Polarization-Mode Coupling for Fiber Coils Materials Development for Advanced Optical Fiber Sensors and Lasers Optoelectronic Fibers Fiber Grating Devices CO2-laser-inscribed long period fiber gratings: from fabrication to applications Micro-/Nano-optical Fiber Devices Measurement of Optical Fiber Grating Measurement of Optical Fibre Amplifier Measurement of Optical Fiber Laser Distributed Rayleigh Sensing Distributed Raman Sensing Distributed Brillouin Sensing: Time-Domain Techniques Distributed Brillouin Sensing: Frequency-Domain Techniques Distributed Brillouin Sensing: Correlation-Domain Techniques Optical Fibre Sensors for Remote Condition Monitoring of Industrial Structures Optical Fiber Sensor Network and Industrial Applications Fibre Optic Sensors for Coal Mine Hazard Detection Optical Fiber Sensors in Ionizing Radiation Environments Polymer Optical Fiber Sensors and Devices Solid Core Single-Mode Polymer Fiber Gratings and Sensors Microstructured Polymer Optical Fiber Gratings and Sensors Polymer Fiber Sensors for Structural and Civil Engineering Applications Photonic Microcells for Sensing Applications Filling Technologies of Photonic Crystal Fibers and Their Applications Photonic Crystal Fiber-Based Grating Sensors Photonic Crystal Fiber-Based Interferometer Sensors Optical Fiber Microfluidic Sensors Based on Opto-physical Effects Micro-/Nano-Optical Fiber Microfluidic Sensors All Optical Fiber Optofluidic or Ferrofluidic Microsensors Fabricated by Femtosecond Laser Micromachining

Signal processing is a widely applied tool for condition monitoring of rotating machinery. These techniques are thus utilized extensively to process experimental signals. However, hypothesis about data and computational efforts often restrict the application of some techniques. The empirical mode decomposition (EMD) and Hilbert spectrum allows to overcome these limitations. This paper applies this method to vibration signal analysis for localised gear fault diagnosis. Considering that the gear fault vibration signal generate both the amplitude and frequency demodulated signals, the EMD could exactly decompose these demodulated signals into a number of intrinsic mode functions (IMFs), each of which can be amplitude-demodulated or frequency-demodulated component, the frequency families could be separated effectively from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when fault occurs in gears, the energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy spectrum can exactly provide the energy distribution of the signal in certain frequency range with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local Hilbert energy spectrum.

Condition monitoring is a significant requirement for ensuring safe and reliable working of machining processes and rotary components. Recent developments in digital signal processing techniques along with emergence of miniature sensors and high-speed data acquisition devices furnish a peculiar opportunity for the development and implementation of effective, in-situ, non-intrusive condition monitoring methods for a broad range of machining processes and rotary components. The selection of most appropriate signal processing technique, best suited for a particular application, is a major challenge in the field of condition monitoring, especially when working in a competitive industrial environment. This problem can only be solved if one has a thorough understanding of various aspects such as which parameter to be monitored, aim of monitoring, processing limitations and possible future scope of such monitoring method. Signal processing methods applicable to machining processes and rotary components have been investigated in relation to the parameters monitored, purpose of monitoring and future scope for that method. Limitations of such processing methods have also been reviewed to make the reader aware of disadvantages in using a particular signal processing method. This paper is intended as a valuable guide for researchers to assist in identification and application of the best possible condition monitoring method for machining processes and rotary components.