Empirical Mode Decomposition of Pressure Signal for Health Condition Monitoring in Waterjet Cutting

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach

Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems

An adaptive-gain super-twisting sliding mode observer is proposed for fault reconstruction in electro-hydraulic servo systems (EHSS) receiving bounded perturbations with unknown bounds. The objective is to address challenging problems in classic sliding mode observers: chattering effect, conservatism of observer gains, strong condition on the distribution of faults and uncertainties. In this paper, the proposed super-twisting sliding mode observer relaxes the condition on the distribution of uncertainties and faults, and the gain adaptation law leads to eliminate observer gain overestimation and attenuate chattering effects. After using the equivalent output-error-injection feature of sliding mode techniques, a fault reconstruction strategy is proposed. The experimental results are presented, confirming the effectiveness of the proposed adaptive super-twisting observer for precise fault reconstruction in electro-hydraulic servo systems.Comment: Final versio

Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components – A Review

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine

Exploring the optimal potential of transient reflection method through mel-frequency ceptrums coefficient and artificial neural network for leak detection and size estimation in water distribution systems

Water pipeline systems are critical infrastructures that provide potable water to communities. The design and operation of these systems are complex and require careful consideration of various factors, such as system reliability. Regular maintenance and inspection of pipelines and other components are necessary to prevent leaks and ensure that the system operates effectively. The efficient detection and accurate estimation of leaks in water distribution systems are crucial for maintaining the integrity and functionality of the infrastructure. This research aims to unleash the full potential of the transient reflection method through the integration of Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN) techniques for leak detection and size estimation in water distribution systems. By leveraging the combined power of signal processing and machine learning, this study aim to advance the state-of-the-art methodologies for leak detection and size estimation, providing more accurate and efficient approaches based on transient reflection method. The objectives of this research are to explores the application of MFCC as a signal processing technique to extract vital information from the transient reflection signals. The transient reflection signals carry valuable insights into the characteristics of the water distribution system and can aid in identifying leaks. Furthermore to investigate and select significant features derived from the transient reflection signals that reflect the nature of leak size. Finally, is to develop and validate an ANN-based model for leak size estimation that harnesses the power of the extracted TRM features. To achieve these objectives, extensive experimentation and analysis will be conducted using transient reflection method obtained from laboratory scale water distribution systems. The data will be collected from various sizes of leaks. The collected dataset will serve as the foundation for training and validating the developed ANN model. Performance evaluation metrics, such as accuracy, precision, recall, and mean squared error, will be utilized to assess the effectiveness and reliability of the leak detection and size estimation technique. The expected outcomes of this research include advancements in leak detection and size estimation techniques in water distribution systems. The integration of MFCC and ANN techniques has the potential to significantly improve the accuracy and efficiency of leak detection, leading to timely identification and mitigation of leaks. The developed estimation model can aid in assessing the severity of leaks, enabling more effective allocation of resources for repair and maintenance activities. Ultimately, the findings of this research will contribute to the enhancement of water distribution system management, promoting water conservation and minimizing the adverse impacts of leaks on infrastructure and the environment. In conclusion, this research endeavors to unleash the full potential of the transient reflection method through the integration of MFCC and ANN techniques for leak detection and size estimation in water distribution systems. By leveraging signal processing and machine learning, this study aims to advance the state-of-the-art methodologies and provide more accurate and efficient approaches to address the challenges associated with leak detection and size estimation. The outcomes of this research have the potential to significantly benefit water management authorities, utilities, and researchers working in the field of water distribution system management and conservation

Adaptive data analysis for damage detection and system identification in civil infrastructure

Time-varying structural systems are often encountered in civil engineering. As extreme events occur more frequently and severely in recent years, more structures are loaded beyond their elastic conditions and may thus experience damage in the years to come. Even if structures remain elastic, energy dissipation devices installed on structures often reveal hysteretic behaviors under earthquake loads. Therefore, it is imperative to develop and implement novel technologies that enable the identification and damage detection of time-varying systems. In this dissertation, adaptive wavelet transform (AWT) and multiple analytical mode decomposition (M-AMD) are proposed and applied to identify system properties and detect damage in structures. AWT is an optimized time-frequency representation of dynamic responses for the extraction of features. It is defined as an average of overlapped short-time wavelet transforms with time-varying wavelet parameters in order to extract time-dependent frequencies. The effectiveness of AWT is demonstrated by various analytical signals, acoustic emission and impact echo responses. M-AMD is a response decomposition method for the identification of weakly to moderately nonlinear oscillators based on vibration responses. It can be used to accurately separate the low and high frequency components of time-varying stiffness and damping coefficients in dynamic systems. The efficiency and accuracy of the proposed M-AMD are evaluated with three characteristic nonlinear oscillators and a 1/4-scale 3-story building model with frictional damping under seismic excitations. Finally, AWT-based M-AMD is applied to decompose the measured dynamic responses of a 1/20-scale cable-stayed bridge model tested on four shake tables and evaluate the progression of damage under increasing earthquake loads --Abstract, page iii

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries