A generalized model selection framework for multi-state failure data analysis

PurposeThe purpose of this paper is to carry out a reliability analysis of a mechanical system considering the degraded states to get a proper understanding of system behavior and its propagation towards complete failure.Design/methodology/approachThe reliability analysis of computerized numerical control machine tools (CNCMTs) using a multi-state system (MSS) approach that considers various degraded states rather than a binary approach is carried out. The failures of the CNCMT are classified into five states: one fully operational state, three degraded states and one failed state.FindingsThe analysis of failure data collected from the field and tests conducted in the laboratory provided detailed understandings about the quality of the material and its failure behavior used in designing and the capability of the manufacturing system. The present work identified that Class II (major failure) is critical from a maintainability perspective whereas Class III (moderate failure) and Class IV (minor failure) are critical from a reliability perspective.Research limitations/implicationsThis research applies to reliability data analysis of systems that consider various degraded states.Practical implicationsMSS reliability analysis approach will help to identify various degraded states of the system that affect the performance and productivity and also to improve system reliability, availability and performance.Social implicationsIndustrial system designers recognized that reliability and maintainability is a critical design attribute. Reliability studies using the binary state approach are insufficient and incorrect for the systems with degraded failures states, and such analysis can give incorrect results, and increase the cost. The proposed MSS approach is more suitable for complex systems such as CNCMT rather than the binary-state system approach.Originality/valueThis paper presents a generalized framework MSS's failure and repair data analysis has been developed and applied to a CNCMT.

Reliability Importance Measures considering Performance and Costs of Mechanical Hydraulic System for Hydraulic Excavators

Although some reliability importance measures and maintenance policies for mechanical products exist in literature, they are rarely investigated with reference to weakest component identification in the design stage and preventive maintenance interval during the life cycle. This paper is mainly study reliability importance measures considering performance and costs (RIMPC) of maintenance and downtime of the mechanical hydraulic system (MHS) for hydraulic excavators (HE) with energy regeneration and recovery system (ERRS) and suggests the scheduled maintenance interval for key components and the system itself based on the reliability R i t . In the research, the required failure data for reliability analysis is collected from maintenance crews and users over three years of a certain type of hydraulic excavators. Minitab is used for probable distribution estimation of the mechanical hydraulic system failure times, and the model is verified to obey Weibull distribution. RIMPC is calculated by multiplying the reliability R i t and weighting factor W i and then compared with other classical importance measures. The purpose of this paper is to identify the weakest component for MHS in the design stage and to make appropriate maintenance strategies which help to maintain a high reliability level for MHS. The proposed method also provides the scientific maintenance suggestion for improving the MHS reliability of the HE reasonably, which is efficient, profitable, and organized.

Quantitative Risk Assessment for Aerospace Facility According to Windrose

Wind direction and speed are the most important factors that determine the degree of damage caused by a jet fire. In this study, the metal hose used to extract/supply fuel was identified as the component with the highest risk for a jet fire occurring at an aerospace facility. A risk assessment was performed to evaluate the individual risk of a jet fire from the metal hose according to the wind direction and speed. HSE failure data was applied for calculating the jet fire probability including metal hose failure, ignition frequency, and jet fire frequency. Which was 3.0 × 10−4. The individual risk of different fatality probabilities was calculated according to the wind rose data for the aerospace facility. The individual risk from jet fire in the aerospace facility was calculated with a maximum risk of 3.35 × 10−5 and a minimum risk of 1.49 × 10−6. The individual risk satisfied HSE ALARP criteria. In addition, firewalls, extinguishing systems, and an emergency shut off system were enhanced, and it was thought that the risk from jet fire could satisfy acceptable criteria.

Design and Development of Smart Parking System Based on Fog Computing and Internet of Things

Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and revenue. The pricing strategy does not achieve high revenue because a fixed pricing rate is utilized. To address these issues, this paper proposes a smart parking system based on the Internet of Things (IoT) that provides useful information to drivers and parking administrators about available parking spots and related services such as parking navigation, reservation, and availability estimation. A multi-layer architecture is developed that consists of multiple sensor nodes, and fog and cloud computing layers. The acquired parking data are processed through fog computing nodes to facilitate obtaining the required real-time parking data. A novel algorithm to obtain the optimal parking spot with the minimum arrival time is also presented. Proof-of-concept implementation and simulation evaluations are conducted to validate the system performance. The findings show that the system reduces the parking arrival time by 16%–46% compared to current parking systems. In addition, the revenue is increased for the parking authority by 10%–15%.

Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

Projected epidemiological consequences of the Omicron SARS-CoV-2 variant in England, December 2021 to April 2022

The Omicron B.1.1.529 SARS-CoV-2 variant was first detected in late November 2021 and has since spread to multiple countries worldwide. We model the potential consequences of the Omicron variant on SARS-CoV-2 transmission and health outcomes in England between December 2021 and April 2022, using a deterministic compartmental model fitted to epidemiological data from March 2020 onwards. Because of uncertainty around the characteristics of Omicron, we explore scenarios varying the extent of Omicron's immune escape and the effectiveness of COVID-19 booster vaccinations against Omicron, assuming the level of Omicron's transmissibility relative to Delta to match the growth in observed S gene target failure data in England. We consider strategies for the re-introduction of control measures in response to projected surges in transmission, as well as scenarios varying the uptake and speed of COVID-19 booster vaccinations and the rate of Omicron's introduction into the population. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020-2021.

Reliability Evaluation of CNC Machine Tools Considering Competing Failures of Fault Failure Data and Machining Accuracy Degradation Data

Traditional reliability evaluation of CNC machine tools usually considers a single failure mode of fault failure or degradation failure, or considers fault failure and degradation failure to be independent of each other. However, in the actual working conditions, fault failure and degradation failure are mutually affected, and the reliability evaluation of the competing failure models of CNC machine tools by considering the two failure modes comprehensively can get more accurate evaluation results. Therefore, this paper proposes a reliability evaluation method for CNC machine tools considering fault failure data competing with machining accuracy degradation data. A fault failure model of CNC machine tools is established based on a non-homogeneous Poisson process. The fault failure model is updated according to the different effects of each maintenance result of the failure on machining accuracy. By integrating multiple geometric errors of CNC machine tools through multi-body system theory, the amount of machining accuracy degradation is extracted. A machining accuracy degradation failure model is established using the Wiener process. Considering the correlation between fault failure and degradation failure, a competing failure model based on the Coupla function is developed for evaluating the reliability of CNC machine tools. Finally, the effectiveness of the proposed method is verified by example analysis.

STABILITY FAILURE ANALYSIS IN THE OPERATIONS OF FLOATING DRY DOCKS

The main area of this work reflects a topic for which there is little or limited reference available and is carried out to meet the needs of professional and practical floating dry dock operators. The risk of hazards in floating dry docks is evaluated using a discrete fuzzy set theory (FST) and an evidential reasoning (ER) approach in a situation where historical failure data is not available. Fuzzy set modelling is used to estimate the safety levels of the causes of basic failure events in floating dry docks due to stability concerns using the concept of linguistic variables, and provides a framework for dealing with such variables in a systematic way. The ER approach is used to synthesise the estimated safety levels of the causes of hazards/basic hazard events. The results of this work will be valuable to dry dock masters and sister maritime engineering professionals.

Reliability Study of Parameter Uncertainty Based on Time-Varying Failure Rates with an Application to Subsea Oil and Gas Production Emergency Shutdown Systems

The failure rate of equipment during long-term operation in severe environment is time-varying. Most studies regard the failure rate as a constant, ignoring the reliability evaluation error caused by the constant. While studying failure data that are few and easily missing, it is common to focus only on the uncertainty of reliability index rather than parameter of failure rate. In this study, a new time-varying failure rate model containing time-varying scale factor is established, and a statistical-fuzzy model of failure rate cumulated parameter is established by using statistical and fuzzy knowledge, which is used to modify the time-varying failure rate model. Subsequently, the theorem of the upper boundary existence for the failure rate region is proposed and proved to provide the failure rate cumulated parameter when the failure rate changes the fastest. The proposed model and theorem are applied to analyze the reliability of subsea emergency shutdown system in the marine environment for a long time. The comparison of system reliability under time-varying failure rate and constant failure rate shows that the time-varying failure rate model can eliminate the evaluation error and is consistent with engineering. The reliability intervals based on the failure rate model before and after modification are compared to analyze differences in uncertainty, which confirm that the modified model is more accurate and more practical for engineering.