Launch Vehicle Reliability Research Articles

Statistical reliability estimation of space launch vehicles: 2000–2022

This research examines the reliability of space launch vehicles (SLVs) performing commercial, civil, and military space lift missions through trend analysis and a variety of statistical methods. Data sets obtained from the Seradata database are analyzed for trends by examining data subsets including launch date, sector (commercial, civil or military), launch country, intended mission orbit, SLV family, and failed subsystem. Evolving, time-dependent first-level Bayesian success rate analysis is performed to assess SLV reliability and performance trends on 1873 launches occurring during the period 1 January 2000 to 31 December 2022. It was determined that even with a near-exponential increase in launch events in the 2020s, the number of failures is less than 6% each year, with the average success rate being 95% annually. Overall, 56% of launches were sent into low Earth orbit (LEO) and 32% were sent into geosynchronous Earth orbit (GEO) or geostationary Earth orbit (GSO). Although SLVs servicing missions to these orbital regimes featured success rates above 92%, LEO launches accounted for approximately 73% of total launch failures, while GEO-GSO launches accounted for 18% of failures. Specifically looking at U.S. and Russian SLV families, the main subsystem responsible for failure was propulsion, which accounted for 72.3% of 47 reported launch failures. First-level and second-level (using method of moments) Bayesian techniques were performed on 37 launch vehicle families and it was found that vehicles with a high accrual of launches performed with a higher reliability than vehicles with a low number of launches, resulting in first-level success rates of greater than 90%.

Machine-Learning and Physics-Based Tool for Anomaly Identification in Propulsion Systems

Launch anomalies occur frequently during the early phase of a program, with many of the anomalies attributed to propulsion systems. Approaches for identifying and mitigating potential propulsion failures can aid development programs and accelerate the resolution of root cause investigations. In reusable systems, anomaly detection methods can be employed to detect latent system health issues that could become problematic as the system ages. Modern launch support relies on human judgement for redline limit generation and visual family data comparison for many operational aspects, which makes it challenging to identify failure modes and to diagnose an anomaly. Additionally, family data comparison is unavailable for the first few launches of a new vehicle. Automated tools to quickly identify system failures of new and reusable systems can bridge these gaps. Physics-based modeling and machine learning (PBMML) offers methods that can improve the reliability of new or reusable launch vehicles by identifying propulsion anomalies or issues before they jeopardize future space missions. PBMML can then be used to inform corrective actions. This paper describes an anomaly data generation module which automates the process of simulating anomalous scenarios in launch vehicle and fluid networks, while a long-term short-term memory network is used to provide real-time anomaly classification on a simplified stage test case.

Difficulties with Replacing Crew Launch Abort Systems with Designed Reliability

As the space industry continues to innovate and new paradigms arise to challenge the status quo, human spaceflight is now perceived as safer and more accessible than ever before. This has led to a new line of thinking in which crewed launch vehicles should be reusable and reliable like commercial airplanes, forgoing the need for an abort system. This paper will counter that line of thought with an analysis of the spectrum of coverage historical crew abort systems provided during launch and use historical data from launch rate successes and failures to glean insight into what reliability in the human spaceflight industry can expect when designing the vehicles of the future. This historical launch vehicle reliability will then be compared to system safety standards used in the commercial aviation industry to understand if future designs truly need a crew abort system. Through this analysis, the rationale for why these crew abort systems have historically been used can be better understood.

Fault detection and diagnosis for liquid rocket engines with sample imbalance based on Wasserstein generative adversarial nets and multilayer perceptron

The reliability of liquid rocket engines (LREs), which are the main propulsion device of launch vehicles, cannot be overemphasised. The development of fault detection and diagnosis (FDD) technology for LREs can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. With the rapid development of artificial intelligence technologies such as machine learning and artificial neural network, data-driven FDD methods have gained increasing attention. However, the scarcity of engine fault samples limits the application of this methods. We proposed a method combining Wasserstein generative adversarial nets (WGANs) and multilayer perceptron (MLP) to perform FDD for LREs with sample imbalance. Wasserstein generative adversarial nets were trained using the fault data from the actual hot-firing ground test of a large LRE. Considerable fault data were generated to expand the data set to balance the ratio of positive and negative samples. Subsequently, the expanded data set was used to train the MLP for FDD of a large LRE. The results showed that the samples generated by the WGAN were authentic, confirming the application of the proposed method as a novel and effective tool for establishing a complete LRE fault database. Furthermore, the diagnosis times of the proposed method on five fault tests were advanced by 0.66, 15.82, 0.24, 0.14 and 1.08 s in relation to those of the conventional red-line cut-off system. Compared with support vector machine and adaptive threshold algorithm, the proposed method also performed better.

Fault Detection and Diagnosis for Liquid Rocket Engines Based on Long Short-Term Memory and Generative Adversarial Networks

The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine’s normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely.

Analysis of Space Launch Vehicle Failures and Post-Mission Disposal Statistics

In a time in which a significant number of launch vehicles are being developed, boosted by numerous new space actors demanding cheaper access to space, the need to ensure the reliability of launch vehicles and the performance of a successful Post-Mission Disposal (PMD) is undeniable. The first step to ensure the reliability of new launch vehicles is to analyse previous launch failures, learning from past mistakes. In this paper, the launch failures that occurred over the past 16 years are analysed. These failures are classified as a function of the subsystems involved in the failure, as well as the phase of the mission in which the failure occurred. As a result, the most critical subsystems are identified for every mission phase. Subsequently, the most critical subsystem is further investigated, and its most critical components are identified. Furthermore, aiming to improve the present PMD practices, an understanding of the current status is necessary. This paper provides an overview of the evolution in terms of mean orbital lifetime of the rocket bodies launched since the year 2000. Moreover, the number of manoeuvres performed and the orbital lifetime saved by these manoeuvres is analysed between 2016 and 2020, further classifying the statistics by launcher family and country.

A Bayesian Network Approach to Launch Vehicle Software Failure Prediction

Launch Vehicle software has played an important role in Launch Vehicle system. However, the reliability assessment of Launch Vehicle software is still a hard problem due to the complexity of Launch Vehicle software. Failure prediction can be an effective approach of Launch Vehicle reliability evaluation, whereas failure prediction of software has not yet been fully explored. In this paper, a Markov Bayesian Network model for Launch Vehicle software failure prediction (MBNG) is proposed. In MBNG, unique features of Launch Vehicle software is considered as an important part in the modeling process, which improves the effectiveness of this novel model. Experiments are conducted to verify the effectiveness of MBNG model and compare its performance with classic models.

Launch vehicle multi-objective reliability-redundancy optimization using a hybrid genetic algorithm-particle swarm optimization

This paper focuses on multi-objective reliability optimization of a two-stage launch vehicle using a hybridized Genetic Algorithm-Particle Swarm Optimization with provisions of relative weighting between the objectives. In this respect, the launch vehicle key subsystems as well as their functions are initially introduced. Subsequently, the system reliability block diagram is constructed using the launch vehicle working order of the subsystems augmented with the requirements for a robust fault/failure tolerant design and performance. Next, based on the proposed reliability block diagram arrangement a bi-objective optimization is formulated to maximize the system reliability while minimizing the launch vehicle cost with design variables being the component reliability, system redundancy level, and the type of components. To examine the validity of the proposed algorithm, a benchmark problem in reliability-redundancy optimization is also investigated through the proposed scheme. Benchmark simulation results indicate that neither genetic algorithm nor the particle swarm optimization alone can produce sufficiently accurate optimal results, but their hybrid combination is more efficient and achieves the desired level of accuracy. Thus the proposed hybrid genetic algorithm-particle swarm optimization is applied to the problem of launch vehicle multi-objective reliability optimization that has resulted in a convex Pareto front, out of which many design points could be selected for implementation. Finally, a Monte Carlo simulation is performed against the subsystem reliability uncertainty in order to analyze the effect of uncertainty on the total launch vehicle reliability and cost.

1.5.2 Emerging Real‐Time Intelligent Agents In Space Launch Verification and Anomaly Resolution

AbstractIn 1997, the Evolved Expendable Launch Vehicle (EELV) program, now under the auspices of the United Launch Alliance (ULA), began with fundamental goals to reduce cost and improve the reliability of launching satellites and interplanetary spacecraft. With payloads costing more that several billion dollars each, the reliability of launch vehicles mandates perfect launches. Subsequently, launch systems have become highly complex with increasing launch rates of satellites to perform surveillance, network‐centric command and control, and communications on the land, sea, air and space. Launch system Ground Computers, Command and Control (GC3) has grown exponentially in software capabilities, collecting and displaying data to domain experts, and transmitting real‐time data to world‐wide support teams. Rapid and accurate anomaly resolution with customers and contractors, especially for events leading up to day‐of‐launch (DOL) involves hundreds of personnel using these complex systems. This paper describes near‐term capabilities which are the building blocks of future intelligent agents: decision making, knowledge management; computer systems, control software and desktop PC tools. These agents are rapidly maturing into integrated systems decision making processes that are responsive within seconds. This near‐term development activity is compared with a 20‐year forecast of spaceport intelligent agent systems.

The relevance of economic data in the decision-making process for orbital launch vehicle programs, a U.S. perspective

Over the past fifteen years, major U.S. initiatives for the development of new launch vehicles have been remarkably unsuccessful. The list is long: NLI, SLI, and X-33, not to mention several cancelled programs aimed at high speed airplanes (NASP, HSCT) which would share some similar technological problems. The economic aspects of these programs are equally as important to their success as are the technical aspects. In fact, by largely ignoring economic realities in the decisions to undertake these programs and in subsequent management decisions, space agencies (and their commercial partners) have inadvertently contributed to the eventual demise of these efforts. The transportation revolution that was envisaged by the promises of these programs has never occurred. Access to space is still very expensive; reliability of launch vehicles has remained constant over the years; and market demand has been relatively low, volatile and slow to develop. The changing international context of the industry (launching overcapacity, etc.) has also worked against the investment in new vehicles in the U.S. Today, unless there are unforeseen technical breakthroughs, orbital space access is likely to continue as it has been with high costs and market stagnation. Space exploration will require significant launching capabilities. The details of the future needs are not yet well defined. But, the question of the launch costs, the overall demand for vehicles, and the size and type of role that NASA will play in the overall launch market is likely to influence the industry. This paper will emphasize the lessons learned from the economic and management perspective from past launch programs, analyze the issues behind the demand for launches, and project the challenges that NASA will face as only one new customer in a very complex market situation. It will be important for NASA to make launch vehicle decisions based as much on economic considerations as it does on solving new technical challenges.

Liquid Propulsion Launch Vehicle Reliability in Closed Form

Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.

Use of optical fibers to increase launch vehicle reliability

Abstract Conventional data buses, telemetry links, and sensors using wire harnesses as the transmission media suffer from numerous shortcomings, especially when utilized in spacecraft. This paper describes fiber-optic networks that could be implemented in launch vehicles in the near term. Special emphasis is placed on the increase in reliability that fiber optics affords over conventional cable/wire approaches.

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