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In fire condition, the time to failure of a timber connection is mainly reliant on the wood charring rate, the strength of the residual wood section, and the limiting temperature of the steel connectors involved in the connection. The purpose of this study is to experimentally investigate the effects of loaded bolt end distance, number of bolt rows, and the existence of perpendicular-to-wood grain reinforcement on the structural fire behavior of semi-rigid glued-laminated timber (glulam) beam-to-column connections that used steel bolts and concealed steel plate connectors.

In total, 16 beam-to-column connections, which were fabricated in wood-steel-wood bolted connection configurations, in eight large-scale sub-frame test assemblies were exposed to elevated temperatures that followed CAN/ULC-S101 standard time-temperature curve, while being subjected to monotonic loading. The beam-to-column connections of four of the eight test assemblies were reinforced perpendicular to the wood grain using self-tapping screws (STS). Fire tests were terminated upon achieving the failure criterion, which predominantly was dependent on the connection’s maximum allowed rotation.

Experimental results revealed that increasing the number of bolt rows from two to three, each of two bolts, increased the connection’s time to failure by a greater time increment than that achieved by increasing the bolt end distance from four- to five-times the bolt diameter. Also, the use of STS reinforcement increased the connection’s time to failure by greater time increments than those achieved by increasing the number of bolt rows or the bolt end distance.

The invaluable experimental data obtained from this study can be effectively used to provide insight and better understanding on how mass-timber glulam bolted connections can behave in fire condition. This can also help in further improving the existing design guidelines for mass-timber structures. Currently, beam-to-column wood connections are designed mainly as axially loaded connections with no guidelines available for determining the fire resistance of timber connections exerting any degree of moment-resisting capability.

This paper aims to detect the printing failures (such as warpage and collapse) in material extrusion (MEX) process effectively and timely to reduce the waste of printing time, energy and material.

The approach is designed based on the frequently observed fact that printing failures are accompanied by abnormal material phenomena occurring close to the nozzle. To effectively and timely capture the phenomena near the nozzle, a camera is delicately installed on a typical MEX printer. Then, aided by the captured phenomena (images), a smart printing failure predictor is built based on the artificial neural network (ANN). Finally, based on the predictor, the printing failures, as well as their types, can be effectively detected from the images captured by the camera in real-time.

Experiments show that printing failures can be detected timely with an accuracy of more than 98% on average. Comparisons in methodology demonstrate that this approach has advantages in real-time printing failure detection in MEX.

A novel real-time approach for failure detection is proposed based on ANN. The following characteristics make the approach have a great potential to be implemented easily and widely: (1) the scheme designed to capture the phenomena near the nozzle is simple, low-cost, and effective; and (2) the predictor can be conveniently extended to detect more types of failures by using more abnormal material phenomena that are occurring close to the nozzle.

The purpose of this study is to find the reliability of the three-component three-phased mission system, which can be repaired by considering the exponential distribution for repair and failure rates in the transitions between the phases based on states with Markov approach. Also, multilevel-phased mission systems are calculated based on states for partially working states.

The reliabilities of the repairable two-level and three-level three-component three-phased mission systems based on states are calculated with the Markov approach. The structure functions are obtained for each phase of the systems, and differential equations are created by the failure and repair of each working state component. These equations are solved using Laplace method.

Reliability values of two-level and three-level three-component three-phased systems with different failure, repair, and time intervals are calculated and compared. The intermediate states that multilevel systems handle differently from two-level systems provide a better investigation of the systems. So, these repairable systems offer transparent information in complex systems like transportation and energy, ensuring appropriate timing and cost for repair operations.

This study is original in terms of calculating the reliability of the repairable phased mission system based on the states using Markov method. It is also important in calculating the reliability of the repairable multilevel phased mission system based on states and making reliability comparisons according to different repair and failure rates, equal and different time intervals.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

Ensuring safe operation of a urea fertilizer plant (UFP) is a vital aspect for its functioning and production. Clearly the safe operation of such systems can only be archived with proper and effective maintenance scheduling and through controlling its failures as well as repairs of the components. Also for this, the concern plant management must have the information regarding the failures that affects the system's performance most/least. The objective of this study is to analyze mathematically the factors that are responsible for the failure/degradation of the decomposition unit of UFP.

The considered system has been modeled by the aid of Markov's birth–death process with two types of failures for its components: variable (which are very similar in practical situations) and constant. The mathematical model is solved by the help of Laplace transform and supplementary variable technique.

In the present paper, the availability, reliability and mean time to failure (MTTF) are computed for the decomposition unit of the UFP. The critical components that affect the reliability and MTTF of the decomposition unit are identified through sensitivity analysis.

In this paper, a mathematical model based on the working of the decomposition unit of a UFP has been developed by considering two types of failure, namely, variable failures rates and constant failure rates (which has not been done in the literature for the decomposition unit). Conclusions in this paper are good references for the improvement of the same.

The software development team reviews the testing phase to assess if the reliability growth of software is as per plan and requirement and gives suggestions for improvement. The objective of this study is to determine the optimal review time such that there is enough time to make judgments about changes required before the scheduled release.

Testing utilizes majority of time and resources, assures reliability and plays a critical role in release and warranty decision-making reviews necessary. A very early review during testing may not give useful information for analyzing or improving project performance, and a very late review may delay product delivery and lead to opportunity loss for developers. Therefore, it is assumed that the optimal time for review is in the later stage of testing when the fault removal rate starts to decline. The expression for this time point is determined using the S-curve 2-D software reliability growth model (SRGM).

The methodology has been illustrated using the real-life fault datasets of Tandem computers and radar systems resulting in optimal review time of 14 weeks and 26 months, respectively, which is neither very early in testing nor very near to the scheduled release. The developer can make changes (more resources or postpone release) to expedite the process.

Most of the literature studies focus on determination of optimal testing or release time to achieve considerable reliability within the budget, but in this study, the authors determine the optimal review time during testing using SRGM to ensure the considerable reliability at release.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.