Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

Welding is widely used in all industries, and its demand is drastically increasing. Today, all sectors of engineering need quality welders to meet their standards. Welders need years of experience and knowledge to meet those standards. They require lots of training, equipment, tools, and safety standards to master the welding skill. The cost of training is very expensive as they will be practicing every day with different materials. The purpose of this project is to train them in a new merged-up environment of the virtual and real world. As a result, this method would reduce training costs and enhance the safety of the users. This method is a medium that helps users to have a better understanding of welding operations and gives them the confidence to perform proficiently in reality along with the elimination of risk, liability, and injury.

The purpose of this paper is to examine the extent to which the Intelligent Enterprise Resource Planning (I-ERP) System can be used in company operations. Machine learning is embedded in a decision tree algorithm to demonstrate the viability of intelligent technology in an ERP system and to enhance the quality of operations through an I-ERP system. The study consists of two steps. In the first step, the algorithm uses the decision tree algorithm to demonstrate the application of intelligent technology in an ERP system. In the second step, the proposed model analyzes four quality criteria related to company operations through I-ERP system in order to determine whether or not I-ERP has significant improvement on managers’ decisions. As a result, the use of I-EPR may improve the quality of operations, agile respond to market demand, increase the efficiency and the competitiveness in organizations. An illustration example is provided to demonstrate the application of I-ERP.

Six Sigma is recognized as an essential tool for continuous improvement of quality. A large num-ber of publications by various authors reflect the interest in this technique. Reviews of literature on Six Sigma have been done in the past by a few authors. However, considering the contributions in the recent times, a more comprehensive review is attempted here. The authors have examined vari-ous papers and have proposed a different scheme of classification. In addition, certain gaps that would provide hints for further research in Six Sigma have been identified. As a results the rela-tionship between Six Sigma, Design for Six Sigma (DFSS), and how these two concepts support the quality system for organizational learning and innovation performance have been discussed that would help researchers, academicians and practitioners to take a closer look at the growth, devel-opment and applicability of Six Sigma in Design.

Sending men to space has never been an ordinary activity, it requires years of planning and preparation in order to have a chance of success. The payoffs of reliable and repeatable space flight are many, including both Commercial and Military opportunities. In order for reliable and repeatable space flight to become a reality, catastrophic failures need to be detected and mitigated before they occur. It can be shown that small pieces of a design which seem ordinary can create devastating impacts if not designed and tested properly. This paper will address the use of a Failure Mode, Effects, and Criticality Analysis (FMECA) with modified Risk Priority Number (RPN) and its application to safety critical design components of shuttle liftoff. An example will be presented here which specifically focuses on the Solid Rocket Boosters (SRBs) to illustrate the FMECA approach to reliable space travel.

Computer TCP/IP networks are becoming critical in all aspects of life. As computer networks continue to improve, the levels of redundancy continue to increase. Modern network redun-dancy features can be complex and expensive. This leads to misconfiguration of the redun-dancy features. Monitoring everything is not always practical. Some redundancy features are easy to detect while others are more difficult. It is common for redundancy features to fail or contribute to a failure scenario. Incorrectly configured redundancy will lead to network downtime when the network is supposed to be redundant. This presents a false sense of se-curity to the network operators and administrators. This research will present two scenarios that are commonly left unmonitored and look at a practical way to deploy solutions to these two scenarios in such a way that the network uptime can be improved. Implementing a practical approach to monitor and mitigate these types of failures allows costs spent on re-dundancy to increase uptime, and thus increase overall quality that is critical to a modern digital company.

The human error has been reported as a major root cause in road accidents in today’s world. The human as a driver in road vehicles composed of human, mechanical and electrical components is constantly exposed to changing surroundings (e.g., road conditions, environment)which deteriorate the driver’s capacities leading to a potential accident. The auto industries and transportation authorities have realized that similar to other complex and safety sensitive transportation systems, the road vehicles need to rely on both advanced technologies (i.e., Advanced Driver Assistance Systems (ADAS)) and Passive Safety Systems (PSS) (e.g.,, seatbelts, airbags) in order to mitigate the risk of accidents and casualties. In this study, the advantages and disadvantages of ADAS as active safety systems as well as passive safety systems in road vehicles have been discussed. Also, this study proposes models that analyze the interactions between human as a driver and ADAS Warning and Crash Avoidance Systems and PSS in the design of vehicles. Thereafter, the mathematical models have been developed to make reliability prediction at any given time on the road transportation for vehicles equipped with ADAS and PSS. Finally, the implications of this study in the improvement of vehicle designs and prevention of casualties are discussed.

Reliability and quality assurance have become major considerations in the design and manufacture of today’s parts and products. Survivability of green compact using powder metallurgy technology is considered as one of the major quality attributes in manufacturing systems today. During powder metallurgy (PM) production, the compaction conditions and behavior of the metal powder dictate the stress and density distribution in the green compact prior to sintering. These parameters greatly influence the mechanical properties and overall strength of the final component. In order to improve these properties, higher compaction pressures are usually employed, which make unloading and ejection of green compacts more challenging, especially for the powder-compacted parts with relatively complicated shapes. This study looked at a mathematical survivability model concerning green compact characteristics in PM technology and the stress-strength failure model in reliability engineering. This model depicts the relationship between mechanical loads (stress) during ejection, experimentally determined green strength and survivability of green compact. The resulting survivability is the probability that a green compact survives during and after ejection. This survivability model can be used as an efficient tool for selecting the appropriate parameters for the process planning stage in PM technology. A case study is presented here in order to demonstrate the application of the proposed survivability model.

In order to prepare students for the workforce, academic programs incorporate a variety of tools that students are likely to use in their future careers. One of these tools employed by business and technology programs is the integration of live software applications such as SAP through the SAP University Alliance (SAP UA) program. Since the SAP UA program has been around for only about 10 years and the available literature on the topic is limited, research is needed to determine the strengths and weaknesses of the SAP UA program. A collaborative study of SAP UA faculty perceptions of their SAP UAs was conducted in the fall of 2011. Of the faculty invited to participate in the study, 31% completed the online survey. The results indicate that most faculty experienced difficulty implementing SAP into their programs and report that a need exists for more standardized curriculum and training, while a large percentage indicated that they are receiving the support they need from their schools and SAP.

Kouroush Jenab, Donald Staub, Mark Rajai PDF (233 K)
Abstract: Students at risk of dropping out of Science, Technology, Engineering, and Mathematics (STEM) programs often display signs that indicate they are at risk. A need exists to identify at risk STEM students early and to develop and implement effective intervention strategies that utilize the Total Quality Management (TQM) approach. Most of all, a database system is needed to track this early intervention process, if retention rates are to be improved. To address this need at a small community college in North Carolina, a system was developed and underwent a pilot study in Fall 2009 and Spring 2010. The two pilot groups were compared to the two control groups to identify differences in retention, course credit completion rates, and grade point averages (GPA). The first pilot group displayed no significant differences, while the second pilot group displayed significant differences in most of the areas analyzed in the study, indicating a database system can be used to improve STEM student retention. While the second of the two pilot groups displayed promising results, managerial and logistical issues, such as less than optimal instructor involvement, impeded success were identified. This paper will describe the design, implementation, and the preliminary results of this study and outlines the need for further research that confirms these preliminary findings.