Major Safety Accidents Research Articles

Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention

Accurate prediction of a roller's remaining useful life (RUL) is significant for a hot strip mill to avoid major safety accidents and substantial economic losses. Since rollers’ degradation processes represent multi-stage characteristics, traditional single-stage models cannot acquire accurate RUL prediction results. Therefore, this paper proposes an adaptive staged RUL prediction method based on multi-scale long short-term memory network with multi-head self-attention (LSTM-MHA). The roller's production data and operation data are fused to construct interpretable health indicators (HI), which can represent the roller's remaining rotatable angle. Then the degradation process is divided into multiple stages and analyzed. The proposed multi-scale LSTM-MHA can adaptively update the model weights based on changes in the roller degradation stages by the multi-scale memory structure. The MHA embedding mechanism filters important temporal information for LSTM units. The trained model can predict the roller's RUL by inputting the current roller's HI. The proposed method is verified on an industrial hot strip mill roller dataset from a well-known steel company. The validation results show that the prediction accuracy of the proposed method is higher than 98.98%. Compared with existing deep learn-based methods, the proposed multi-scale LSTM-MHA method has significant advantages in roller's RUL prediction.

The dynamic evolution of powder flow and wall normal stress in different flow pattern silos

Major safety accidents involving silos in industrial processes are closely associated with abnormal wall stresses. The effects of different flow patterns and particle velocity distribution on wall stresses are investigated during silo discharge. The existence of particle velocity retardation layer near the wall boundary in the mass flow was observed by tracer particles and high-speed camera. The silo discharge undergoes a transformation from funnel flow to mass flow at a hopper half angle of 30°. As the outlet diameter increases, the time point of the flow pattern transformation becomes more and more later. The evolution of particle velocity in the central of the funnel flow is related to the outlet velocity wave propagation and the V-shaped surface expansion. The relationship between stress fluctuations and velocity characteristics is established. The flow channel simultaneously expands upwards as the velocity wave propagates and generates a periodic fan-shaped velocity wave at the top. The formation of stress concentration zone at the bin/hopper transition was observed.

Integrated micro thermoelectric devices with self-power supply and temperature monitoring: Design and application in power grid early warning

The Internet of Things (IoT) and wearable technology have led to the development of wireless sensors at an unprecedented pace. Within the IoT framework, smart grid systems require real-time temperature monitoring of various switch contacts. Especially in the process of power transformation (China: ultra-high voltage → 10 kV → 380 / 220 V), poor switch contact will lead to overheating, resulting in equipment damage and major safety accidents. Micro-thermoelectric devices (micro-TEDs) exhibit a significantly greater thermoelectromotive force than metal-based thermocouples. By establishing two bivariate first-order equations for hot-end and cold-end temperatures, accurate detection of both temperatures and simultaneous self-power supply can be achieved. In this work, we observe that the Seebeck coefficient of hot extruded n-type Bi2Te2.7Se0.3 and p-type Bi0.5Sb1.5Te3 thermoelectric couple exhibits slight fluctuations around 435 μV/K, while its resistivity follows an almost linear trend with temperature from room temperature to 120 °C. By utilizing these two bivariate first-order equations, the temperatures at both the hot and cold ends can be determined. Based on this, we have fabricated a micro-TED (size: 14 × 6 × 2.5 mm3, TE leg: 0.8 × 0.8 × 1.6 mm3) with a temperature measurement accuracy of up to ± 0.1 °C and a power density of 10.12 mW/cm2 under a temperature difference of 30 °C. Furthermore, we used thirteen micro-TEDs and one circuit board to build a self-power supply temperature monitor. When this TE sensor device was deployed on the plum blossom connector surface and conducted online assessments for the 10 kV substation over a span of approximately 200 h, it continuously outputs a temperature signal and the power supply maintained normalcy, exhibiting no aberrations. This temperature monitoring system can be extended to other fields that require all-weather wireless temperature monitoring, which provides a new direction for the development of micro-TEDs.

High-speed rail defect detection using multi-frequency exciting 3D ACFM

High load wheel-rail friction leads to surface defects on the rail, which requires an accuracy non-destructive testing (NDT) technology to prevent rail fracture and even major safety accidents. Alternating current field measurement (ACFM) has attracted attention due to its high detection precision and low sensitivity of lift-off and velocity effects. The paper proposes a multi-frequency, three-dimensional (3D) ACFM detection system that can obtain abundant detection information for multi-frequency comprehensive evaluation to improve detection accuracy. The system mainly includes an expandable phase-locked amplifier module and an adjustable multi-frequency signal source module, which provides a hardware IQ demodulation acceleration platform for multi-frequency and multi-channel ACFM detection to obtain enough defect signals for analysis. Analysis of the speed effect indicates that increasing the excitation frequency can improve the stability of the detection signal. We design a quantitative evaluation model for defects with a depth of 2–8 mm and surface angle between 30° and 75°.

A study on the application of Prepar3D-based Aviation’s Localizer Performance with Vertical Guidance Approach procedures in the teaching of simulated flight training courses

Firstly, the advantages of flight simulators and the three types of flight simulation software widely used by current flight students are briefly described, focusing on the analysis of instrument landing knowledge, how to read the instrument approach procedure charts, and how to use the flight simulation software to implement various instrument landing systems, so as to make the correct operation according to the approach procedure charts. Based on Prepar3D flight simulation software, Cessna C172R G1000 system was used to demonstrate two different types of precision approaches, so that the trainees could be more clearly and intuitively familiarized with the relevant operation procedures of precision approaches. From the results of the demonstration, it can be seen that flight simulation has the advantages of improving training efficiency, saving training funds, ensuring flight safety, and reducing environmental pollution, etc. The advantages of flight simulation software can fundamentally improve the flight skills of the trainees, increase flight experience, and thus reduce the incidence of major safety accidents.

Bolt looseness identification using a knocking acoustic signal

As a common failure form of the bolted joint, bolt looseness can lead to relative slip between the connected surfaces. Under the action of shear external forces, the bolt experiences fatigue failure and fracture, which can affect the service performance and connection strength of the equipment, and even cause major safety accidents. At present, good results have been achieved in detecting the tightness of bolt connections through Structural Health Monitoring (SHM) technology. However, most of them are still carried out under laboratory conditions and cannot be applied in engineering. Therefore, this article proposes looseness recognition of bolts by knocking acoustic signals. This method converts the acoustic time series signal into a Mel-Frequency Cepstral Coefficient (MFCC) frequency spectrum and then it recognizes the bolt looseness status through the Mobilenetv3 deep neural network. The results show this method can identify loose bolts. Meanwhile, it has a strong engineering application background.

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Bolt-ball joints are widely used in space structures, and their looseness may lead to major safety accidents. The current bolt monitoring methods based on deep learning usually have high computational complexity, and it is difficult to guarantee its computational efficiency under practical scenario. To mitigate this problem, here in this paper, an efficient robotic-assisted bolt-ball joint looseness monitoring approach using convolutional block attention module (CBAM)-enhanced lightweight ResNet is proposed. Firstly, the robotic-assisted tapping method is applied to bolt-ball joints to generate audio signals, which are constructed into time-frequency maps by continuous wavelet transform. Secondly, the original ResNet is improved as a lightweight network, which successfully reduces model complexity, and employs time-frequency maps as input. Then, CBAM is introduced to capture global information and focus on the critical feature. Thus, the efficiency of feature extraction is significantly improved. Finally, by the overall optimized structure, a CBAM-enhanced lightweight ResNet model is established to monitor the bolt-ball joints looseness state accurately. Experimental results demonstrate the high efficiency while maintaining very lightweight structure of the proposed method, verifying the effectiveness and superiority of the robot-assisted approach using CBAM-enhanced lightweight ResNet over other methods.

A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.

Limit load prediction analysis of X80 pipeline containing corrosion in mountainous landslide section

Currently in China X80 pipeline laying mostly concentrated in mountainous areas, oil and gas pipeline transmission distance, through the complex and diverse terrain, the pipeline by the buried environment will not only appear corrosion defects, but also by the impact of soil slippage to reduce the pressure-bearing capacity of the pipeline, resulting in pipeline perforation, broken, leading to oil and gas leakage accidents, further leading to fire, explosion and environmental pollution and other major safety accidents. In view of the important strategic position of oil and gas pipelines and the severity of accident hazards, it is important to study the ultimate bearing capacity of buried corroded pipelines. With this purpose, this paper analyzes the existing pipe-soil coupling theories based on the large-scale test in the field, optimizes the nonlinear contact model, and establishes a nonlinear corroded pipe-soil interaction model and verifies its accuracy. By using the existing soil load application function and coupling the actual soil slip data, the inhomogeneous movement of soil slip is realized. Based on the existing corrosion size characterization methods, the effect of corrosion size on the ultimate load carrying capacity of intact pipes was further verified by means of a combination of FEA and experiments. Specifically, the effects of soil slip under no load and internal pressure coupled conditions on the mechanical behavior of corroded pipelines are compared and analyzed; the necessity of considering soil slip is clarified; the influencing factors such as pipe corrosion size, operating internal pressure, and corrosion position are analyzed to reveal the failure mechanism of buried corroded pipelines in mountainous areas. By using the pipe displacement at the strain failure as the evaluation index, and using the dimensional analysis method to reveal the relationship between each influencing factor and the ultimate load, the ultimate load prediction formula for corroded pipelines under soil slip actions is derived. The research results can help evaluate the safety condition of existing operating pipelines, release early warning for the structure safety of pipelines, and provide guidance for the safety protection of pipelines in mountainous areas.

Review of research on intelligent diagnosis of oil transfer pump malfunction

Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation, and its working reliability directly affects the safety of oil and gas storage and transportation. Intelligent diagnosis is a key technical method to reduce failure rate of oil transfer pump, ensure the safety of gathering and transportation process, and avoid major safety accidents caused by oil transfer pump failure. Various oil transfer pumps have been emerged in recent decades, and the common fault types and characteristics of oil transfer pump have been brought out in the review. This article highlights on the research of the fault signal and processing methods of oil transfer pump. Firstly, the fault signal of the oil transfer pump is discussed and the advantages and disadvantages of different signal extraction are analyzed. Secondly, the intelligent diagnosis method of oil transfer pump and the shortcomings of the existing methods are pointed out. Finally, the conclusions are given and the future development perspectives of oil transfer pumps are suggested. The main contribution of this review is to give a syn-thetic understanding on oil transfer pumps.

Study on the Propagation and Conversion of Evanescent Lamb Waves in Discontinuous Plate Structures

In conventional Lamb wave based non-destructive testing (NDT), subwavelength imaging cannot be performed for very small damages due to diffraction limits, which may pose safety hazards to engineering structures and large equipment, and even cause major safety accidents, leading to property damage and casualties. However, in evanescent Lamb waves, there is the necessary subwavelength information that can be converted into propagable Lamb waves, which contain subwavelength damage information that can be received to form super-resolution imaging. Therefore, this article investigates the propagation and transformation characteristics of evanescent Lamb waves in a discontinuous plate structure, providing a research approach for further super-resolution imaging, which has certain scientific research and practical application value.

A method for calculating the void fraction of two-phase leakage ports of storage tanks

The problem of leakage of superheated liquids such as LPG, LNG, LCG and AML is widely valued in the process industry. The key to superheated liquid storage and transportation is to prevent cracks due to unexpected situations such as tank overpressure, pipe corrosion and violent collisions. When the tank wall ruptures, the air pressure inside the tank drops sharply and a violent phase change occurs in the degraded liquid inside the tank to maintain energy balance. The liquid instantly evaporates into a gas, releasing a huge amount of energy to form a flash, and this leakage can lead to violent phase changes and two-phase flash jets containing large amounts of liquid droplets and steam, and further lead to major safety accidents such as fires, explosions and the spread of toxic substances. In this work, the pressure drop in the tank during a leak and the phase change at the leak caused by the pressure change at the orifice are investigated through theoretical analysis and experiments. Based on the ideal gas equation of state, mass conservation, gas-liquid non-slip and thermodynamic equilibrium, the pressure drop inside the tank and the phase change at the orifice during the whole leakage process are modelled and validated, and the results show that the model is in good agreement.

A Quantitative Analysis of Chemical Plant Safety Based on Bayesian Network

Once a chemical production accident occurs in a chemical plant, it often causes serious economic losses, casualties, and environmental damage. Statistics show that many major accidents in the production and storage of chemicals are mainly caused by human factors. This article considers the influence of the human factor and proposes a quantitative analysis model of a chemical plant based on a Bayesian network. The model takes into account the main human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. The Bayesian network modeling method and simulation were used to predict the safety quantitative value and safety level of the chemical plant. Using this model, we can quickly calculate the safe quantitative ratio of each factor in the chemical plant. Through the safety quantitative value, safety level, and sensitivity analysis, the safety hazards of chemical companies can be discovered. Immediate improvements of potential safety hazards in chemical plants are very effective in preventing major safety accidents. This model provides an effective method for chemical park managers to monitor and manage chemical plants based on quantitative safety data.

Multiagent Game of Intelligent Building Detection and Its Harm Rumor Analysis

The “14th Five‐Year” development plan of the construction industry of the Chinese government makes it clear that intelligent buildings are the general direction of future construction development. However, the occurrence of construction accidents in recent years has brought huge casualties. With the rapid development of third‐party inspection institutions, their judgment has become an important threshold for the access to smart buildings. The failure of inspection institutions to detect the renewal of leases may lead to the failure of intelligent systems, leading to the failure of construction equipment and causing major safety accidents such as the uncontrolled fall of construction elevators and the fall of tower crane items. As the supervisor of third‐party inspection institutions, government functional departments are responsible for the failure of intelligent systems. All of them undertake the important role of intelligent building security. This paper builds an evolutionary game model with government regulatory authorities, third‐party testing institutions, and intelligent construction enterprises as the three game parties, based on variables that affect the factors of the players, and discusses the possible scenarios of the stabilization strategy, which draws the following points: (1) the increase of incentives and penalties by the government is conducive to promoting the construction of high‐quality intelligent buildings and the normative behavior of the third party without intention to seek rent, but the increase of incentives will not be conducive to the government fulfilling its regulatory responsibilities. (2) The reasonable reward and punishment mechanism set by the government must meet the condition that the sum of rewards and punishments for all parties is greater than their speculative returns so as to ensure the safety of intelligent buildings in the evolving and stable market environment. (3) The accountability of the superior government for the dereliction of duty of the regulatory authorities is of great significance to enhancing the stability of enterprises in building high‐quality intelligent buildings. (4) It is also an effective way to avoid the construction of low‐quality smart buildings by improving the listed sales income of enterprises and increasing the rent‐seeking cost of enterprises. Combined with the improved SIR model, it is calculated that after the occurrence of a construction accident, when the rumor first appears, the rumor will quickly spread out of control without timely management and supervision. The conclusion of the game model provides a great reference value for the government’s regulatory behavior. The quality of the government’s regulatory behavior is directly related to the future of high‐quality development of intelligent construction and also expands the application field of evolutionary game theory and SIR theory. The government should actively manage third‐party testing institutions and intelligent building enterprises before construction accidents happen. After the accident, strict management and supervision should be carried out on well‐known bloggers before the emergence of rumors so as to reduce the possibility of standardizing individuals’ reprinting of rumors and avoid the uncontrollable negative impact in the later period, thus seriously affecting the credibility of government supervision.

Interval-Valued-Based Stacked Attention Autoencoder Model for Process Monitoring and Fault Diagnosis of Nonlinear Uncertain Systems

The measurement data fluctuate up and down within an interval centered on the true value due to disturbances and noise which are zero mean. Meanwhile, traditional industrial process monitoring algorithms are mostly based on normal data for modeling and rarely consider fault information. As a result, the variables involved in the modeling process may contain information irrelevant to the fault, thereby leading to the degradation of monitoring performance. On the basis of the above considerations and to avoid the occurrence of major safety accidents, this study proposes a stacked attention autoencoder (SAAE) monitoring model in view of the upper and lower bounds of the interval and fault–related variables. Based on the viewpoint that some sampled variables’ distribution will change after the fault occurs, Jensen–Shannon (JS) divergence is used as an indicator to measure the difference, and the variables with significant changes before and after the fault are screened out. Subsequently, an attention mechanism (AM) is introduced in the process of training stacked AE (SAE). Thus, the features with a strong correlation with the just-screened fault–related variables have a larger weight. In other words, the model focuses more on the fault–related features. This method not only reduces the influence of uncertainty, but also uses historical fault data to pick out fault–related information. This study demonstrates the performance of the algorithm through the Tennessee Eastman (TE) process.

Application of anticorrosive materials in cement slurry: Progress and prospect

During gas well cementing operation, the erosion of acidic formation corrosive medium will destroy the cementation between the cement slurry and the sidewall in the well sealing section, reduce the mechanical properties of the cement paste, cause problems such as sidewall collapse and casing damage, seriously endanger the normal exploitation of oil and gas resources, and cause major safety accidents. Therefore, improving the corrosion resistance of cement paste is the key to ensuring long-term stable cementing of high-temperature sour gas wells. This paper summarizes the influencing factors, corrosion mechanism, corrosion test methods and research status of anti-corrosion oil well cement additives, discusses the advantages and disadvantages of each anti-corrosion additive, summarizes the latest progress and challenges of anti-corrosion oil well cement, and aims to provide some reference for researchers in related fields.

An equipment multiple failure causes intelligent identification method based on integrated strategy for subway sliding plug door system under variable working condition

The subway sliding plug door system has been opened and closed frequently for a long time under variable working conditions, and multiple failures are prone to occurring and resulting in train shutdowns and even major safety accidents. Due to the complex physical mechanism of the sliding plug door system, it is difficult for a single signal to accurately represent the failure states of the sliding plug door system. Thus, identifying the multiple failure causes of the subway sliding plug door system has become a challenging problem. Aiming at the problem, an equipment multiple failure causes intelligent identification method based on an integrated strategy is proposed for the subway sliding plug door system under variable working condition. In the proposed method, firstly, the sensitive features that can reflect the degradation state of equipment are obtained by using the random forest to measure the importance of fetatures and sort them. Secondly, feature dimensionality is reduced by using t-distributed stochastic neighbor mbedding (TSNE) to map the screened high-dimensional features to low-dimensional space. Finally, the parameters of the extreme learning machine (ELM) are optimized by using the particle swarm optimization (PSO) algorithm to build a multiple failure causes classification model. The proposed method is verified by the 1:1 benchmark test data of the subway sliding plug door system. The results show that the proposed method has higher classification accuracy, faster calculation speed, and stronger generalization ability. The proposed method is an effective integrated strategy to identify multiple failure causes in the subway sliding plug door system and guide the health management and operational maintenance of the subway sliding plug door system.

Effect of Winding Steel Wire on the Collapse Pressure of Submarine Hose

The submarine hose plays a vital role in the single-point mooring system and is a necessary channel for medium transportation. Once crushed under the load of the seawater external pressure, it will cause oil and gas leakage and major safety accidents. It is a composite hose composed of a rubber layer, cord layer and steel helix wire, of which the steel helix wire plays an important part in bearing mechanical properties. In this work, python language was used to model the submarine hose parametrically, the finite element (FE) analysis software ABAQUS was utilized to analyze the ultimate bearing capacity of the hose under uniformly distributed external pressure loads and the influence of the initial ovality of the submarine hose, the diameter and pitch of the helix wire, the yield strength of the helix wire material on the ultimate bearing capacity of the submarine hose were studied. Through a large number of FE results, the ultimate bearing capacity of the hose was obtained by fitting the prediction formula.

Study of oxidation behavior and tensile properties of candidate superalloys in the air ingress simulation scenario

Air ingress incidents are major safety accidents in very-high-temperature reactors (VHTRs). Air containing a high volume fraction of oxygen may cause severe oxidation of core components at the VHTR, especially for the significantly thin alloy tube wall in the intermediate heat exchanger (IHE). The research objects of this study are Inconel 617 and Incoloy 800H, two candidate alloys for IHE in VHTR. The air ingress accident scenario is simulated with high-temperature air flow at 950 °C. A continuous oxide scale was formed on the surfaces of both the alloys after the experiment. Because the oxide scale of Inconel 617 has a loose structure, whereas that of Incoloy 800H is denser, Inconel 617 exhibited significantly more severe internal oxidation than Incoloy 800H. Further, Inconel 617 showed a significant decrease in ultimate tensile strength and plasticity after aging for 200 h, whereas Incoloy 800H maintained its tensile properties satisfactorily. Through control experiment under vacuum, we preliminarily concluded that serious internal oxidation is the primary reason for the decline in the tensile properties of Inconel 617.

Memory Linked Anomaly Metric Learning of Thermography Rail Defects Detection System

With the development of railway, high speed and intensive rolling will significantly increase the probability of fatigue damage and they lay a hidden danger for major safety accidents. Therefore, it is vital to detect defects of the rail. The commonly used algorithms mostly trained with the defect samples whereas this data is rare and cannot cover all types of defects. On the contrary, the background samples are relatively easy to obtain. In this work, a new algorithm is proposed which models the background of rail samples with memory items in order to exploit the full use of the background information. The samples that do not fit with the models are considered as defect candidates. Our model is only trained with the normal samples and a small amount of defect samples. When new patterns occur, they will be considered as defect candidates and the model is not limited to the known defect patterns. Additionally, the proposed metric learning modules enable the model to learn representative memory items, and enlarges the feature space distance between defect and normal samples as well as reducing the influence of disturbance. In order to show the efficacy of the proposed algorithm, it has been validated with various types of rail defects, ranging from the visually apparent defects to visually weak defects. In the experiments, the thermography inspection system scans at a constant speed while inductively heats the rail tracks continuously to record the infrared thermal image sequences from the rail surface. Recent anomaly detection algorithms are used as platform for objective performance evaluation. The algorithms are evaluated quantitatively based on Area under ROC curve (AUC) at both image level and pixel level along with the visual comparative analysis.