VPLS Based Quality and Cost Control for Tennessee Eastman Process

Product quality and operation cost control obtain increasing emphases in modern chemical system engineering. To improve the fault detection power of the partial least square (PLS) method for quality control, a new QRPV statistic is proposed in terms of the VP (variable importance in projection) indices of monitored process variables, which is significantly advanced over and different from the conventional Q statistic. QRPV is calculated only by the residuals of the remarkable process variables (RPVs). Therefore, it is the dominant relation between quality and RPV not all process variables (as in the case of the conventional PLS) that is monitored by this new VP-PLS (VPLS) method. The combination of QRPV and T2 statistics is applied to the quality and cost control of the Tennessee Eastman (TE) process, and weak faults can be detected as quickly as possible. Consequently, the product quality of TE process is guaranteed and operation costs are reduced.

Quality Based Prioritized Sensor Fault Monitoring Methodology

To improve the detection and identification performance of the Statistical Quality Monitoring （SQM） system, a novel quality based Prioritized Sensor-Fault Detection （PSFD） methodology is proposed. Weighted by the Vp （variable importance in projection） index, which indicates the importance of the sensor variables to the quality variables, the new monitoring statistic, Qv, is developed toensure that the most vital sensor faults be detected successfully. Subsequently, the ratio between the Detectable Minimum Faulty Magnitude （DMFM） of the most important sensor and of the least important sensor is only gpmin/gpmax 〈〈 1. The Structured Residuals are designed according to the Vp index to identify and then isolate them. The theoretical findings are fully supported by simulation studies performed on the Tennessee Eastman process.

Off-Line Quality Control in Bioproducts Processing: A Case Study in Greece

In the present article we study the production of grape molasses. Data drawn from a specified biolaboratory, are properly analyzed in order to detect factors that affect significantly the Brix value and the volatile acidity of the final product. The ground that is used for planting and a variety of grapes have been taken into account. Off-line statistical quality control techniques have been employed and the outcomes are displayed and discussed in detail.

Engineering Quality and Knowledge Systems： An Appraisal for Improving the Spinning Stage

The industrial organisations have a vested interest in quality and its practices in making a quantum leap, thereby requiring proper refocusing and rethinking. This is targeted seriously towards firms acquiring Knowledge Systems （KS） in order to nurture the employees＇ capabilities. Therefore, to enhance performances and their highly creative direction takes consideration of QE （quality engineering） and KS and disciplines complementary to each other. Additionally, the flow of operations and activities of the KS through the application of quality engineering （QE） principles is according to the view of Japanese engineer Taguchi. This aims to achieve good quality in the product design and processes, and also to anticipate problems that may occur before the shipping of the products. In this study, the use of quality control engineering and Taguchi＇s function methods in determining these properties and the loss caused by the company will be examined. However, the adopted approach of study scope to the society will aid KS. Also, efforts in this direction motivate the employment of a statistical method by Taguchi which relied on the principle of target-oriented quality, representing the value characterising the product quality.

Memory-Type Control Charts Through the Lens of Cost Parameters

A memory-type control chart utilizes previous information for chart construction.An example of a memory-type chart is an exponentially-weighted moving average(EWMA)control chart.The EWMA control chart is well-known and widely employed by practitioners for monitoring small and moderate process mean shifts.Meanwhile,the EWMA median chart is robust against outliers.In light of this,the economic model of the EWMA and EWMA median control charts are commonly considered.This study aims to investigate the effect of cost parameters on the out-of-control average run lengthðARL_(1)Þin implementing EWMA and EWMA median control charts.The economic model was used to compute the ARL_(1) parameter.The 14 input parameters were identified and the analysis was carried out based on the one-parameter-at-a-time basis.When the input parameters change based on a predetermined percentage,the ARL_(1) is affected.According to the results of the EWMA chart,nine input parameters had an effect andfive input parameters had no effect on the ARL_(1) parameter.Further,only seven of the 14 input parameters had an effect on the ARL_(1) of the EWMA median chart.However,the effect of each input parameter on the ARL_(1) was different.Moreover,the ARL_(1) for the EWMA median chart was smaller than the EWMA chart.This analysis is crucial to observe and determine the input parameters that have a significant impact on the ARL_(1) of the EMWA and EWMA median control charts.Hence,practitioners can obtain an overview of the influence of the input parameters on the ARL_(1) when implementing the EWMA and EWMA median control charts.

Adaptive Optimal Capacity Perception and Control for Wireless Multi-Hop Networks

In wireless multimedia communications, it is extremely difficult to derive general end-to-end capacity results because of decentralized packet scheduling and the interference between communicating nodes. In this paper, we present a state-based channel capacity perception scheme to provide statistical Quality-of-Service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 wireless multi-hop networks. The proposed scheme first perceives the state of the wireless link from the MAC retransmission information and extends this information to calculate the wireless channel capacity, particularly under a saturated traffic load, on the basis of the interference among flows and the link state in the wireless multi-hop networks. Finally, the adaptive optimal control algorithm allocates a network resource and forwards the data packet by taking into consideration the channel capacity deployments in multi-terminal or multi-hop mesh networks. Extensive computer simulations demonstrate that the proposed scheme can achieve better performance in terms of packet delivery ratio and network throughput compared to the existing capacity prediction schemes.