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This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

For manufacturing systems which are in continuous operation and subject to breakdown, inspection can be an appropriate maintenance strategy. In this situation, inspection can reduce down‐time and increase system reliability. In this paper two main ideas are proposed. In the first, an inspection effect function is introduced which modifies the traditional system failure rate distribution. This modification involves a formula which demonstrates the effect of inspection frequency and inspection effectiveness on system failure rate distribution. It is then argued that under inspection policy the system’s traditional failure rate is necessarily affected by these factors. The second idea presents a maintenance model in which the system is interrupted in its time to failure by inspections. Optimisation of this model determines an optimal inspection frequency which minimises the system’s total down‐time. Thus, it is shown that by optimising inspection frequency system availability can be increased.

Baker Hughes INTEQ, a mid‐size manufacturing operation, has successfully developed and implemented an innovative, visual, in‐process inspection programme that can be used on any of the firm's highly technical products. This method of logical and systematic inspection creates a reliable, cost‐effective product geared towards today's fast‐paced manufacturing market, providing an alternative to expensive automated inspection equipment. This quality system monitors and records the assembly operation while allowing for continuous improvement to an already tight process control. The first step uses the flowchart method to teach assembly/inspection personnel a logical process of 100% inspection to replace random checking of the assembly. The next step introduces self‐inspection by systematically removing conventional inspection operations over a short period; the results are faster output, improved first time acceptance rates (less rework), and a higher quality product. For companies implementing visual in‐process inspection there are a number of factors to consider: temporary vs permanent employees, employee attitude, and workmanship standards. Implementation of this programme has been highly effective, reducing rework costs by approximately $10,000 per month. This paper explains how the visual, in‐process inspection programme was developed and implemented and how it can be used on any highly technical product. The programme is designed around a manufacturing cell (work area) concept that gives ‘power to the employees’ and leaves out the ‘traditional’ quality/manufacturing process.

The initial excitement over increased solder joint densities, higher manufacturing throughput, and superior electrical performance brought forth by surface mount technology (SMT) has been replaced by frustrations over lower yields and the inherent difficulties of inspecting hidden solder joints. In the plated through hole (PTH) process, rework and inspection tasks were not only relatively easier tasks, but also less costly. The high cost of inspecting and reworking SMT assemblies dictates a rethinking of the assembly process. Increasing first time yields becomes the key to reducing SMT inspection and rework costs. In a high volume facility, a 100% visual inspection process is not feasible because of the high cost of inspection and rework. However, if a company intends to remain competitive, inspection and rework must be reduced without a sacrifice to final product quality. Realising that it is not possible to ‘inspect’ quality into a product, improved yield must result from a controlled process environment. By maintaining a controlled environment, one will be provided with lower inspection costs, lower rework costs, lower scrap and, in the final analysis, improved product quality. At the heart of any process control environment should be a real‐time process control system designed specifically to accommodate SMT process defects. Process monitoring is accomplished by locating and identifying SMT process flaws. These flaws will then be reported to a host system for statistical analysis. These are statistical data used to make timely adjustments to the various stages of the assembly process in a real‐time manner. Being able to monitor the production process objectively in real time, and detect hidden flaws accurately, are the keys to having a successful process inspection system. Automated X‐ray Inspection is gaining acceptance as a viable process monitoring tool, capable of detecting and reporting SMT process flaws, including those hidden flaws not reported with typical visual inspection systems. The purpose of this paper is to show how an Automated X‐ray Inspection system can be integrated into the SMT production process as a cost‐effective method for improving SMT yield.

The purpose of this paper is to model an inspection maintenance strategy that is superior to existing strategies and which incorporates the inspection factor.

An appropriate inspection maintenance strategy can help prevent breakdown failure and reduce breakdown and repair costs. However, every inspection also incurs costs. The optimal model for an inspection maintenance strategy would be such that the inspection frequency closely follows the hazard rate of the equipment over its lifetime.

A model with a three‐layered structure needs to be developed. Over time, equipment can be expected to demonstrate a hazard rate that is decreasing, constant or increasing with time. In the first stage a model for inspection frequency is developed that is time‐dependent and can address this requirement. Second, the hazard rate has a dynamic relationship with the inspection frequency, i.e. as the inspection frequency increases, the hazard rate is known to decrease. In the second stage this is incorporated into the model, using the inspection factor. In the last stage a model for costing is superimposed for cost optimization.

The model developed in this paper provides maintenance managers with a powerful practical tool for determining the optimal inspection frequency for equipment over its lifetime.

Models inspection co‐ordination of a group of machines using a non‐linear integer program. The objective of the model is to minimize the expected total cost per unit time, which is a function of a major inspection cost, minor inspection cost and the cost of failure, subject to the restriction that the inspection interval of each machine is an integer multiple of a basic time interval. Proposes a solution procedure to solve the inspection co‐ordination problem. Uses a numerical example to illustrate the model and its proposed solution method.

Sampling plans for inspection by attributes contain rules for passing from normal to tightened inspection. Usually only the operating characteristic curve (O.C.) will apply but in tightened inspection, lots are unlikely to remain in tightened inspection and inspection will be terminated. This means that the normal and tightened O.C. curves are boundary curves and the true O.C. curve is a composite of both. This article presents a method for calculating the composite O.C. curve and proposes three criteria to compare switching rules.

As the complexity of the multi-component products increases the quality of these products becomes increasingly difficult to control throughout the supply chain. The first step to manufacturing a quality product is to ensure that the product components from suppliers meet specifications. Product quality can be controlled through sampling inspection of the components. The paper aims to discuss these issues.

The model presented in this paper was developed to determine the optimal sampling levels for incoming lots containing parts for production and assembly of multi-component systems. The main objective of the model is to minimize the expected cost that is associated with a nonconforming item reaching assembly.

In this research, the results showed that even with limited time available for inspection, performing sampling inspection significantly reduced the expected cost of a nonconforming item reaching assembly. The model, solved by the evolutionary algorithm, was able to provide a meaningful, near optimal solution to the problem.

In this model the time available for inspection is limited, the distribution of defects is assumed to follow the binomial distribution, and the distribution of accepting the lot with defects follows the hypergeometric distribution. In addition, the inspection is considered to be accurate and, if a nonconforming item is found in the inspected sample, the entire lot is rejected. An example is given with real world data and the results are discussed as they relate to supply chain management and quality.

To describe and then review the Toronto food inspection to describe that inspection system and to assess the mandated completion rates, compliance rates, impact of food‐handler training and levels of standardization in inspection and enforcement activities.

The study included a review and summary of information relating to compliance inspections as well as analysis of data stored in the Toronto Healthy Environments Information System using descriptive and inferential statistics.

Several efficiencies were identified including a 17 per cent increase in completion rates between 2001 and 2003; increased compliance with regulatory requirements from 78 per cent in 2001 to 88 per cent in 2003; a reduction in infractions known to be associated with food‐borne illness; and greater compliance in food premises with certified food handlers compared with those without.

All public health units in Ontario, Canada, conduct routine inspection of food establishments to determine compliance with the Ontario Food Premises Regulations, but few of them disclose the results.

Disclosure of inspection results offers an incentive to operators to comply with the regulations and provides an opportunity to consumers to make informed purchasing choices. Furthermore, investment in food‐handler training and certification programs have long‐term positive implications for food safety.

The purpose of the paper is to show that modern markets are characterized by rapidly changing environments and numerous external forces. Under these conditions, product lifetimes are rapidly reducing. Therefore, products require optimum inspection policies to maintain high quality and reduce costs in the competitive market. This study aims to establish optimal inspection policies of reliability analysis for quantal‐response product with Weibull lifetime components.

This study considers a product consisting of m different components in series with lifetimes that follow Weibull distributions, and applies a competing failure model to examine the proposed series system for quantal‐response products. The maximum likelihood estimators of parameters of the Weibull distribution are derived based on the quantal‐response data in the proposed series system. The statistical features of the model are illustrated through a numerical example of two‐component series products, and the properties of the maximum likelihood estimators were studied via Monte Carlo simulation under a two‐stage inspected scheme for various sampling sizes and inspection time conditions.

Simulation results demonstrate not only that the optimum inspection condition is the inspection times at T₁=0.2 and T₂=0.5 for the two‐stage inspected scheme, but also that the economical sampling size is 150 for both cases.

This research results can be applied to the analysis of one‐shot products, e.g. firework, ammunition, airbag, injector, with Weibull components lifetime distribution or the stockpile storage test.