Reliability and Quality Control

Reliability is the probability that a product will perform satisfactorily for a specified time under the stated operating conditions. This implies probability, duration, and a specification of what is considered satisfactory performance, which necessarily incorporates the use environment. By comparison, quality control is the determination, by measurements, that production materials and processes are within the specified tolerances. Reliability is a design function, quality control a manufacturing function. Both are essential to satisfactory product performance. 

Basic to any design is an accurate understanding of what is desired. Because cost increases with reliability, good design and engineering mandate that only the necessary level of reliability be specified. This requires rigorous analysis of user needs so that a quantitative performance specification can be developed. Performance specifications state what is needed to satisfy the system requirements. They include the environment of use, performance requirements, and the stipulated reliability. 

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Kluger, P. 1964 Evaluation of the Effects of Manufacturing Processes on Structural Design Reliability. In Proceedings 10th National Symposium on Reliability and Quality Control, IEEE, Washington, 538-544. 

The EUSAAR project of the Sixth Framework Programme of the European Commission is one of the steps towards a reliable and quality-controlled network of measurements [16]. The EUSAAR project improved and homogenized 20 European sites for measuring aerosol chemical, physical, and optical properties following a standardized protocol of instrument maintenance, measurement procedures, and data delivery in common format to a common database. EUSAAR also provided intercomparison and calibration workshops as well as training for the station operators. The work started in the EUSAAR is continued in ACTRIS infrastructure of the Seventh Framework Programme of the European Commission. 

Dhillon, B. S., Reliability and Quality Control Bibliography on General and Specialized Areas, Beta Publishers, Gloucester, Ontario, Canada, 1992. 

Hence both the SIL and Beta factor methods have underlying philosophical concerns. The SIL approach uses unveriflable judgments about the relationship between system reliability and quality control techniques and measures, whereas the Beta factor approach draws unveriflable correlations between single-channel failure rates and common-mode failure rates. 

Meister, D., The problem of human-initiated failures. Proceedings of the 8th National Symposium on Reliability and Quality Control, 1962, pp. 234-239. 

Baldzs, A., Szalai, S., Varhalmi, L. A multipurpose computer for Mars space missions. Fifth lAESTED Int. Conf. Reliability and quality control, Lugano, pp. 132-143 (1989)... 

Lipp, J. P. 1957. Topology of switching elements versus reliability. Transactions on IRE Reliability and Quality Control 7 21-34. 

The previous chapters of this book have discussed the many activities which laboratories undertake to help ensure the quality of the analytical results that are produced. There are many aspects of quality assurance and quality control that analysts carry out on a day-to-day basis to help them produce reliable results. Control charts are used to monitor method performance and identify when problems have arisen, and Certified Reference Materials are used to evaluate any bias in the results produced. These activities are sometimes referred to as internal quality control (IQC). In addition to all of these activities, it is extremely useful for laboratories to obtain an independent check of their performance and to be able to compare their performance with that of other laboratories carrying out similar types of analyses. This is achieved by taking part in interlaboratory studies. There are two main types of interlaboratory studies, namely proficiency testing (PT) schemes and collaborative studies (also known as collaborative trials). 

It is important that personnel understand how to achieve safe operation, but not at the exclusion of other important considerations, such as reliability, operability, and maintainability. The chemical industry has also found significant benefit to plant productivity and operability when SIS work processes are used to design and manage other instrumented protective systems (IPS), such as those mitigating potential economic and business losses. The CCPS book (2007) Guidelines for Safe and Reliable Instrumented Protective Systems discusses the activities and quality control measures necessary to achieve safe and reliable operation throughout the IPS lifecycle. 

The Chem Master Workstation is a gas chromatography and gas chromatography-mass spectrometry data-processing system that speeds the flow of data through the laboratory and provides essential quality-assurance and quality-control review. It is a PC-based integrated hardware/ software system that converts gas chromatographic and gas chromatography-mass spectrometric data into reliable analytical reports. 

Performing a thorough method validation can be a tedious process, but the reliability of the data generated with the method is linked directly to the application of quality assurance and quality control protocols, which must be followed assiduously (Table 6.5). 

The error of an analytical result is related to the (in)accuracy of an analytical method and consists of a systematic component and a random component [14]. Precision and bias studies form the basis for evaluation of the accuracy of an analytical method [18]. The accuracy of results only relates to the fitness for purpose of an analytical system assessed by method validation. Reliability of results however has to do with more than method validation alone. MU is more than just a singlefigure expression of accuracy. It covers all sources of errors which are relevant for all analyte concentration levels. MU is a key indicator of both fitness for purpose and reliability of results, binding together the ideas of fitness for purpose and quality control (QC) and thus covering the whole QA system [4,37]. 

Sets of instructions that detail the procedures designed to reduce errors occurring during analytical procedures and ensure accurate quantitations are found in the quality assurance (QA) and quality control (QC) manuals. Quality assurance procedures are used by the laboratory to detect and correct problems in analytical processes. As newer methods and instrumentation are added to the laboratory, older procedures must be modified or changed completely. Quality control procedures are used to maintain a measurement system (i.e., a gas chromatograph) in a statistically satisfactory state to ensure the production of accurate and reliable data. 

In general, the greatest resolution can be obtained in estimates of current or recent historical (within the last 10 to 15 years) emissions. This is because reliable data on fuel use (both quality and quantity) and other activity levels are available, and good estimates of emission coefficients and control efficiencies are available. As one goes further back in time, the data needed for detailed emission estimates are either not available or are less reliable. Recently, SOp and nitrogen oxide emissions were estimated for EPA for the period 1900 to 1980 at the state level by fuel and source sector (4J1). It was particularly difficult to obtain reliable estimates of pre-1940 fuel use and quality, control efficiency, and emission coefficients. Obviously, the less data that are available, the simpler the methodology that must be used. A discussion of a data set required for detailed analysis of emissions and deposition is beyond the scope of this paper, but is available elsewhere (6). 

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