Background and Objective: Pipeline Inspection gauge operation which is called “ pigging operation” is a practical online monitoring method for maintaining the Pipeline in a healthy and safe condition. This operation requires high man-machine interactions. Therefore, it is sensitive to human error which can lead to catastrophic consequences, including human mortality. Materials and Methods: This descriptive study was carried out to assess the probability of human error in pigging operation in a gas transmission plant in Iran. Initially, hierarchical task analysis was conducted via walking through the pigging operation, observing the pigging operation tasks, and doing interviews with operators working in the operation. Subsequently, the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) was utilized to assess the human error probability. Results: In this study, SPAR-H method was implemented on “ pigging operation” . The mean value of human error probability (HEP) was estimated at 0. 218. It should be noted that the mean value of HEP was regarded as a subtask of “ opening bypass valve for decreasing pressure” , as well as a subtask with the highest probability of human error (0. 841). Conclusion: The SPAR-H method is a useful tool for practitioners to assess HEP. According to the obtained results, some of the preventive actions to reduce HEP include the utilization of more clarified instructions for performing operations as well as the automation of the launching and receiving pigging cups.

One of the dangers of storing materials in storage tanks is the leakage of materials due to corrosion to the surrounding environment, which in addition to cause damage due to the loss of valuable material and environmental pollution, can lead to accidents. PCM method is one which is used for inspecting Pipelines by electromagnetic method. Electromagnetic fields easily pass-through soil, water, asphalt, etc. Therefore, without drilling and through the ground, Pipelines are inspected and monitored. By measuring the amount of induced alternating current in its strong magnetic sensors, it is able to detect the current amount in Pipelines, the position of sacrificial anodes, the quality of coating, and the location of its defects. In this method, alternating current is used to inspect Pipelines. By increasing the frequency of alternating current, the inductive effect of this method increases on foreign structures and adjacent metal structures. In this case, by using different frequencies as well as very low frequencies, they eliminate the impact of adjacent foreign structures on the Inspection results.

Magnetic Flux Leakage (MFL) is an indirect measurement technique. Therefore, calibration curves are generally used to estimate the depths of the defects from the measured MFL signals. This has been shown to give good results on varying degrees of the single defects. However due to the interaction between the leakage fluxes, nearby pittings can-not be discriminated and properly assessed using the conventional MFL technique. In order to ensure reliable measurement for this case, the MFL technique is combined with the optical Inspection technique. The main contributions of this study are to develop a new calibration method based on the defect depth, defect area as well as the amplitude of the corresponding MFL signal and propose a novel combined approach for detection and identification of the nearby pittings. MFL and optical Inspection techniques are applied to a test specimen containing the nearby pittings. The results obtained from the experimental tests demonstrate the efficacy of the proposed approach.

Blades play a vital role in the performance of wind turbine systems. However, they are prone to damage from complex and irregular loading, which can lead to catastrophic degradation and costly maintenance. Defects or damage to wind turbine blades (WTBs) not only reduce the lifetime and efficiency of wind turbine electricity generation but also increase monitoring errors, safety hazards, and maintenance costs. Therefore, damage detection for WTBs is of high importance for preventing failures, planning maintenance, and ensuring the operational stability of wind turbines. This review article provides a comprehensive overview of advanced damage detection techniques for WTBs, including many updated methods based on strain measurement, acoustic emission, ultrasound, thermography, and machine vision. With the development of machine vision approaches and image processing technology, machine vision-based detection has become a promising and primary technique for detecting surface damage and measuring the deformation of WTBs [28] due to its low cost, ease of operation, and no need for prior knowledge about damage locations. In this review article, common damages to WTBs are introduced comprehensively at first. In the second stage, the principles of detection, development methods, advantages, and disadvantages of the aforementioned techniques for blade Inspection are examined. The trend in damage detection in wind turbines is moving towards methods that offer full capabilities, long-range, contactless, non-destructive, wireless, and online monitoring. Timely detection of blade damage and continuous assessment of the structural health of wind turbines are increasingly important. This review article proposes a new idea for the design and integration of energy harvesters and WTB damage detection methods, in which the devices are automatic and wireless. Finally, potential research directions, and WTB damage detection techniques through comparative analysis, are reviewed and concluded. This review is expected to provide guidelines for practical WTB Inspections as well as research perspectives for damage detection techniques.

Background: Pigging operation is one of the maintenance activities that is used to check Pipeline functionality in operational conditions using a PIG device and high pressure of liquid/gas, which is potentially hazardous. Objectives: The present study aimed at customizing SPAR-H methodology for the pigging operation using Bayesian networks (BNs). It also aimed at identifying and analyzing human errors in pigging operation in a gas transmission company. Methods: The current article was composed of two main steps. In the first step, the SPAR-H BN model was developed using expertelicited prior probabilities of pigging operation applied to Bayesian network. In this step, CPTs of PSF nodes are constructed using prior probabilities, which are achieved from expert opinion. The CPT of error node is developed using coding process of SPAR-H formula in a simulation node. In the second step, a descriptive study was carried out to estimate the probability of human errors in pigging operation in a gas transmission plant in Iran. First, hierarchical task analysis (HTA) was conducted by walking through the pigging operation and interviews with workers. Next, the SPAR-H BN model was utilized for estimation of human error probability. Results: The developed model was tested on the pigging operation subtasks. In the considered case study, the mean probability of human error was estimated as 0. 184. The highest probability of human error was related to “ opening the kicker valve for enhancing pressure” subtask. Conclusions: The BNs were helpful to adapt the SPAR-H methodology to the pigging operation using dedicated prior probabilities. Beside that, the probabilities of human error can be updated taking into account the more realistic operational and environmental conditions.

Police forces are responsible for inspecting houses as executive officers of the justice system. Sometimes ambiguities in rules and regulations and sometimes incompetent officers can lead to deviances from justice. Besides, ambiguities in rules and regulations can result in the officer’s hesitation in performing their missions. This study attempts to examine the relevant rules analytically and critically to detect possible weak points and introduce certain approaches efficient in erradicating them.

Objective: In this paper, we introduce a new model in the interesting domain of deteriorating item inventory control models. In our model, we consider Inspection and Inspection errors in which literature models are noticeably rare. Methods: We conduct a vast literature study resulting in a proper understanding of literature shortcomings, and develop a new model via operational research model building methods. To solve and analyze the model we implement analytical continuous methods. The MATLAB (R2016b) software tools are used for sensitivity analysis and further study. Results: The assumptions of Inspection and Inspection errors in deteriorating item inventory control models lead to a more realistic model. Also, numerical results show the importance and effect of such assumptions. Conclusion: We provide a number of profit increasing managerial insights based on values of order quantity, Inspection time, replenishment period, etc., via the solution and sensitivity analysis of the new model.