Analysis of acoustic emission data for bearings subject to unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance

A study of new and advanced control charts for two categories of time related processes

Ph.DDOCTOR OF PHILOSOPH

Damage identification in civil engineering infrastructure under operational and environmental conditions

Tese de doutoramento. Engenharia Civil. Faculdade de Engenharia. Universidade do Porto. 201

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Monitoring Hospital Safety Climate Using Control Charts of Non-harm Events in Reporting Systems

The primary aim of this thesis is to design an approach and demonstrate a methodology to supplement safety culture assessment efforts. The framework affords an enhanced understanding of hospital safety climate, specifically reporting culture, through the use of control charts to monitor non-harm patient safety events documented in reporting systems. Assessing safety culture and climate remains difficult. One of the most common methods to assess safety culture is a self-report survey administered annually. Surveys assess safety climate, because they are a snapshot of the management\u27s and front-line staff\u27s perceptions of safety within their settings. One component of safety culture is reporting culture, which is assessed by survey questions targeting the total number and frequency of events reported by individuals. Surveys use subjective data to measure outcome variables with regard to patient safety event reporting. Relying on subjective data when organizations also collect data on actual reporting rates may not be optimal. Additionally, the time lag limits management\u27s ability to efficiently assess the need for, and the effect of improvements. Strategic interventions may result in effective change, but annual summary data may mask the effects. Additionally, there are advantages to focusing on non-harm events, and capturing non-harm event reporting rates may aid safety climate assessment. Despite the limitations of reporting systems, incorporating actual data may allow organizations to gain a more accurate depiction of the safety climate and reporting culture. With the increased prevalence of reporting systems in healthcare organizations, the data can be used to track and trend reporting rates of the organization. Incorporating control charts can help identify expected non-harm event reporting rates, and can be used to monitor trends in reporting culture. Data in reporting systems are continuously updated allowing quicker assessment and feedback than annual surveys. The methodology is meant to be prescriptive and uses data that hospitals typically collect. Hospitals can easily follow the summarized approach: check for underlying data assumptions, construct control charts, monitor and analyze those charts, and investigate special cause variation as it arises. The methodology is described and demonstrated using simulated data for a hospital and three of its departments

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda

Physics-based and Data-driven Methods with Compact Computing Emphasis for Structural Health Monitoring

This doctoral dissertation contributes to both model-based and model-free data interpretation techniques in vibration-based Structural Health Monitoring (SHM). In the model-based category, a surrogate-based finite element (FE) model updating algorithm is developed to improve the computational efficiency by replacing the FE model with Response Surface (RS) polynomial models in the optimization problem of model calibration. In addition, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed RS models. This methodology is applied to a numerical case study of a steel frame with global nonlinearity. Its performance in presence of measurement noise is compared with a method based on sensitivity analysis and it is observed that while having comparable accuracy, proposed method outperforms the sensitivity-based model updating procedure in terms of required time. With the assumption of Gaussian measurement noise, it is also shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. This is validated through several parametric sensitivity studies performed on numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parametersIn the model-free category, this dissertation presents data-driven damage identification and localization methods based on two-sample control statistics as well as damage-sensitive features to be extracted from single- and multivariate regression models. For this purpose, sequential normalized likelihood ratio test and two-sample t-test are adopted to detect the change in two families of damage features based on the coefficients of four different linear regression models. The performance of combinations of these damage features, regression models and control statistics are compared through a scaled two-bay steel frame instrumented with a dense sensor network and excited by impact loading. It is shown that the presented methodologies are successful in detecting the timing and location of the structural damage, while having acceptable false detection quality. In addition, it is observed that incorporating multiple mathematical models, damage-sensitive features and change detection tests improve the overall performance of these model-free vibration-based structural damage detection procedures. In order to extend the scalability of the presented data-driven damage detection methods, a compressed sensing damage localization algorithm is also proposed. The objective is accurate damage localization in a structural component instrumented with a dense sensor network, by processing data only from a subset of sensors. In this method, first a set of sensors from the network are randomly sampled. Measurements from these sampled sensors are processed to extract damage sensitive features. These features undergo statistical change point analysis to establish a new boundary for a local search of damage location. As the local search proceeds, probability of the damage location is estimated through a Bayesian procedure with a bivariate Gaussian likelihood model. The decision boundary and the posterior probability of the damage location are updated as new sensors are added to processing subset and more information about location of damage becomes available. This procedure is continued until enough evidence is collected to infer about damage location. Performance of this method is evaluated using a FE model of a cracked gusset plate connection. Pre- and post-damage strain distributions in the plate are used for damage diagnosis.Lastly, through study of potential causes of damage to the Washington Monument during the 2011 Virginia earthquake, this dissertation demonstrates the role that SHM techniques plays in improving the credibility of damage assessment and fragility analysis of the constructed structures. An FE model of the Washington Monument is developed and updated based on the dynamic characteristics of the structure identified through ambient vibration measurement. The calibrated model is used to study the behavior of the Monument during 2011 Virginia earthquake. This FE model is then modified to limit the tensile capacity of the grout material and previously cracked sections to investigate the initiation and propagation of cracking in several futuristic earthquake scenarios. The nonlinear FE model is subjected to two ensembles of site-compatible ground motions representing different seismic hazard levels for the Washington Monument, and occurrence probability of several structural and non-structural damage states is investigated