Haileyesus B. Endeshaw

Probabilistic Model-Based Prognostics Using Meshfree Modeling

Improved system reliability and reduced maintenance cost are guaranteed if the prediction of remaining useful life (RUL) is deemed to be accurate. Energy systems, like wind turbines, are the primary beneficiaries of this achievement as they tend to suffer from an unexpected early life failure of components that resulted in the loss of revenue and high maintenance costs. The issue of uncertainty in the prediction of a future state is yet a prevailing issue in prognostics and due attention is paramount. Hence, there is a need for establishing a comprehensive framework to quantify uncertainty in prognostics and this research addresses this issue by considering a research question that reads ‘can uncertainty considerations improve the prediction of RUL?’ The following specific aims were developed to answer the research question: (1) develop a meshfree cantilever beam with uncertainty in loading conditions, and (2) predict remaining useful life reliably. A probabilistic framework was developed that efficiently predicts remaining useful life of a component using a combination of meshfree model and degradation model. To account for prediction uncertainty, modeling and loading uncertainties are quantified and incorporated into the framework. As an example, the problem of a cantilever beam subjected to a fatigue loading was considered and local radial point interpolation method was used to find the stresses. The cyclic stresses and the damage model, constructed using the S-N equation, are implemented in the prognostics framework to predict the RUL. Uncertainties in the RUL were quantified in terms of probability density functions, cumulative distribution functions, and 98% confidence limit. The prognostics framework is flexible and can be used as a starting point for RUL prediction of other physical phenomena such as crack propagation, by incorporating more sources of uncertainties in order to make it comprehensive.

A Probabilistic Model-Based Prognostics Using Meshfree Modeling: A Case Study on Fatigue Life of a Cantilever Beam

Accurate prediction of remaining useful life (RUL) will improve reliability and reduce maintenance cost. Therefore, prognostics is essential to predict the RUL of systems and components. However, a big issue of uncertainty prevails in prognostics due to the fact that prognostics pertains to prediction of future state, which is affected by uncertainty. While various researches have been done in areas of prognostics and health management, they lack to perform RUL predictions efficiently. There is a need for an efficient comprehensive framework for quantifying uncertainty in prognostics. The research question to this study is: can meshfree modeling be used in probabilistic prognostics to efficiently predict RUL? The specific aims developed to answer the research question are (1) develop a computational framework for probabilistic prognostics of a fatigue life of a component using meshfree modeling, and (2) perform case study analyses on fatigue life of a cantilever beam. A probabilistic framework was developed that efficiently predicts the RUL of a component using a combination of the meshfree method known as local radial point interpolation method and a fatigue degradation model. Loading uncertainty is quantified and employed in the framework. The computational framework is easily customizable and computationally efficient and, hence, aids in decision making and fault mitigation. As a case study, the RUL of a cantilever beam under plane stress subjected to fatigue loadings was analyzed. Uncertainties in the RUL were quantified in terms of probability density functions, cumulative distribution functions, and 98% bounds of confidence interval. Sensitivity analysis was studied and computational efficiency of the framework was also investigated using first order reliability method and Monte Carlo method. When compared to the Monte Carlo method, first order reliability method provides reasonably good results and is found to be computationally more efficient.Copyright

Reduction of Chatter Using a Probabilistic Approach

Industrial robots are widely used in numerous industrial plants usually to perform repetitive, difficult or hazardous tasks such as polishing, grinding, milling, deburring, and welding. However, their relatively low stiffness prohibits the robots from machining of metallic materials accurately. Besides, the resulting vibration causes reduced tool life. One of the main problems with robot machining is chatter. Although many researchers have studied this problem for a while, they were not able to fully eliminate chatter due to the complexity of the problem which arises from many factors including cutting parameters, cutting tool and work-piece material. Several studies have been conducted to predict the occurrence of chatter. However, many of them lack the important element in predicting chatter: the element of uncertainty. It is important to consider uncertainty in predicting chatter since the parameters associated are inherently uncertain. This study implements probabilistic approach to predict chatter considering the uncertainty in machining parameters. The magnitude of cutting force is used to determine whether a chatter has occurred or not. The research question of this study is ‘Can a probabilistic analysis be used to predict chatter occurrence?’ To answer the research question, the following specific aims were developed: (1) develop a framework for predicting chatter occurrence, and (2) perform probabilistic analysis of chatter occurrence. The framework consists of a multibody modeling software (ADAMS), probabilistic analysis software (NESSUS) and MATLAB which is used as an interfacing platform. The probabilistic analysis implemented in the framework provides two important results. Firstly, it determines the probability of occurrence of chatter. Secondly, it provides sensitivity analysis that shows most important parameters which instigate chatter.Copyright

Uncertainty in Communication with a Sketch

Sketches are one of the main tools for the communication of design ideas during the conceptual phase of the design process. In design communication, one of the major problems is the uncertainty associated with imprecisely defined sketches. There is a need to understand the uncertainty in the communication with sketches. This motivated the formulation of the research question: can uncertainty in communication with a sketch be quantified? To answer the research question, three specific aims were developed, namely, (1) determine the ranking of the features in a sketch, (2) determine the probability of importance of the features in a sketch, and (3) quantify the uncertainty of a sketch using Shannon’s normalized entropy. This paper demonstrates the effective use of the established framework for the quantification of uncertainty and contributes to the improvement of design communication with a sketch.

Probabilistic Approach to Determine the Efficiency of Ocean Wave Energy Conversion Systems

Recently, several research studies are being conducted on Ocean Wave Energy Conversion (OWEC) systems that convert wave motion into electricity. The efficiency of OWEC systems is a parameter of great significance. Thus, it is important to consider factors that affect the efficiency of the system, such as ocean wave properties, airflow characteristics of the OWEC systems, and manufacturing inaccuracies. However, these parameters have inherent uncertainties, which should not be disregarded. Without considering these uncertainties, the feasibility assessment of an OWEC system can be misleading. For this study, the following research question was developed: can probabilistic analysis be used to determine the reliability of the efficiency of the OWEC system? To answer this research question, the specific aims developed were: (1) to quantify the uncertainties of the input parameters, and (2) to determine the reliability of efficiency of the OWEC system. The reliability of the efficiency of the system was calculated probabilistically. Probabilistic sensitivity analysis was performed to determine the random variable that had the most impact on the reliability of the efficiency of the OWEC system. Advanced Mean Value (AMV) method was used to determine the probability of failure. NESSUS software was used to perform the probabilistic analysis. In order to assess the reliability of the system, a target efficiency was set and the probability of achieving this efficiency under uncertain input conditions was investigated. The deterministic and probabilistic results were also compared. The results showed that, probabilistic analysis approach can reveal the reliability of the system efficiency by accounting for the wave parameter and manufacturing uncertainties. Moreover, sensitivity analysis showed the variables that had the most impact on reliability of efficiency.Copyright

Determining Probability of Importance of Features in a Sketch

Sketches are the main tools for the communication of concepts among design team’s members during the ideation phase of the design process. Imprecisely defined sketches lead to uncertainty in communication during the design process. Thus, as a contribution to reduce the uncertainty in design communication, an initial framework for the quantification of uncertainty associated to sketches was presented in previous work. In that initial framework, the probabilities of the features in a sketch were determined based on the assessment of an experienced designer. This approach reduced the usability of the framework by professionals with limited experience e.g. entry-level engineers. Thus, this posed the need of an improved framework and brought the following research question: Can a probabilistic method be used to improve the quantification of uncertainty in sketches? Accordingly, to answer this research question the following specific aims were established: 1) Ranking of features in a sketch, 2) Determination of the probability of importance of features in a sketch, and 3) Quantification of uncertainty in a sketch. The first aim focused on determining and classifying the features in a sketch, based on a hierarchical approach. The second aim focused on determining the probability of importance of the features in a sketch, by assessing its probability of likeliness using an object recognition approach, and by applying a probability transformation. The third aim focused on the quantification of the uncertainty in a sketch, based on the calculation and normalization of the sketch’s entropy. This resulted in the development of an improved framework for the quantification of uncertainty in sketches, which can be used by design practitioners with limited experience, and whose application is presented and detailed in a case study.Copyright

Modeling of a Piezoelectric Energy Harvester Mounted on a Quick-Return Mechanism

Piezoelectric materials are being used to harvest mechanical energy from ambient vibration and convert it to electrical energy. They are mainly used to power miniature wireless sensors such as accelerometers, tachometers and proximity probes, which are commonly used for machine monitoring applications. However, exciting a piezoelectric cantilever with its resonance frequency for maximum power output remains to be a challenge. This is because the natural frequency of piezoelectric cantilevers is much higher than the common ambient vibrations. This study answers the research question: “Does a quick-return mechanism enhance the power output of a piezoelectric energy harvester?” For this purpose, analytical methods were employed to model a piezoelectric energy harvester mounted on a quick-return mechanism. The proposed mechanism was able to generate approximately 13.5mW of power, which is 35%–75% greater than the existing designs. A study on the working frequency range of the harvester for maximum power output was employed by varying the dimensional parameters of the quick-return mechanism. It was determined that by varying the dimensions of the quick return it is possible to harvest maximum power at a range of excitation frequencies. It was demonstrated that the system can effectively produce the maximum power when excited at frequencies ranging from 2rad/s to 46rad/s.Copyright