Detection of unanticipated faults for autonomous underwater vehicles using online topic models

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Field Robotics 35 (2018): 705-716, doi:10.1002/rob.21771.For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.Office of Naval Research Grant Number: N00014‐14‐1‐0199; David and Lucile Packard Foundatio

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

An integrated diagnostic architecture for autonomous robots

Abstract unavailable please refer to PD

Unsupervised anomaly detection for underwater gliders using generative adversarial networks

An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an automated fault detection system remains challenging for several reasons including limited data transmission via satellite services. Currently, most anomaly detection for marine autonomous systems, such as underwater gliders, rely on intensive analysis by pilots. This study proposes an unsupervised anomaly detection system using bidirectional generative adversarial networks guided by assistive hints for marine autonomous systems with time series data collected by multiple sensors. In this study, the anomaly detection system for a fleet of underwater gliders is trained on two healthy deployment datasets and tested on other nine deployment datasets collected by a selection of vehicles operating in a range of locations and environmental conditions. The system is successfully applied to detect anomalies in the nine test deployments, which include several different types of anomalies as well as healthy behaviour. Also, a sensitivity study of the data decimation settings suggests the proposed system is robust for Near Real-Time anomaly detection for underwater gliders

Shallow neural networks for autonomous robots

The use of Neural Networks (NNs) in modern applications is already well established thanks to the technological advancements in processing units and Deep Learning (DL), as well as the availability of deployment frameworks and services. However, the embedding of these methods in robotic systems is problematic when it comes to field operations. The use of Graphics Processing Units (GPUs) for such networks requires high amounts of power which would lead to shortened operational times. This is not desired since autonomous robots already need to manage their power supply to accommodate the lengths of their missions which can extend from hours to days. While external processing is possible, real-time monitoring can become unfeasible where delays are present. This also applies to autonomous robots that are deployed for underwater or space missions. For these reasons, there is a requirement for shallow but robust NN-based solutions that enhance the autonomy of a robot. This dissertation focuses on the design and meticulous parametrization complemented by methods that explain hyper-parameter importance. This is performed in the context of different settings and problems for autonomous robots in field operations. The contribution of this thesis comes in the form of autonomy augmentation for robots through shallow NNs that can potentially be embedded in future systems carrying NN processing units. This is done by implementing neural architectures that use sensor data to extract representations for event identification and learn patterns for event anticipation. This work harnesses Long Short-Term Memory networks (LSTMs) as the underpinning framework for time series representation and interpretation. This has been tested in three significant problems found in field operations: hardware malfunction classification, survey trajectory classification and hazardous event forecast and detection

RootPath: Root Cause and Critical Path Analysis to Ensure Sustainable and Resilient Consumer-Centric Big Data Processing under Fault Scenarios

The exponential growth of consumer-centric big data has led to increased concerns regarding the sustainability and resilience of data processing systems, particularly in the face of fault scenarios. This paper presents an innovative approach integrating Root Cause Analysis (RCA) and Critical Path Analysis (CPA) to address these challenges and ensure sustainable, resilient consumer-centric big data processing. The proposed methodology enables the identification of root causes behind system faults probabilistically, implementing Bayesian networks. Furthermore, an Artificial Neural Network (ANN)-based critical path method is employed to identify the critical path that causes high makespan in MapReduce workflows to enhance fault tolerance and optimize resource allocation. To evaluate the effectiveness of the proposed methodology, we conduct a series of fault injection experiments, simulating various real-world fault scenarios commonly encountered in operational environments. The experiment results show that both models perform very well with high accuracies, 95%, and 98%, respectively, enabling the development of more robust and reliable consumer-centric systems

Risk analysis and decision making for autonomous underwater vehicles

Risk analysis for autonomous underwater vehicles (AUVs) is essential to enable AUVs to explore extreme and dynamic environments. This research aims to augment existing risk analysis methods for AUVs, and it proposes a suite of methods to quantify mission risks and to support the implementation of safety-based decision making strategies for AUVs in harsh marine environments. This research firstly provides a systematic review of past progress of risk analysis research for AUV operations. The review answers key questions including fundamental concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future research trends to bridge existing gaps. Based on the state-of-the-art research, a copula-based approach is proposed for predicting the risk of AUV loss in underwater environments. The developed copula Bayesian network (CBN) aims to handle non-linear dependencies among environmental variables and inherent technical failures for AUVs, and therefore achieve accurate risk estimation for vehicle loss given various environmental observations. Furthermore, path planning for AUVs is an effective decision making strategy for mitigating risks and ensuring safer routing. A further study presents an offboard risk-based path planning approach for AUVs, considering a challenging environment with oil spill scenarios incorporated. The proposed global Risk-A* planner combines a Bayesian-based risk model for probabilistic risk reasoning and an A*-based algorithm for path searching. However, global path planning designed for static environments cannot handle the unpredictable situations that may emerge, and real-time replanned solutions are required to account for dynamic environmental observations. Therefore, a hybrid risk-aware decision making strategy is investigated for AUVs to combine static global planning with dynamic local re-planning. A dynamic risk analysis model based on the system theoretic process analysis (STPA) and BN is applied for generating a real-time risk map in target mission areas. The dynamic window algorithm (DWA) serves for local path planning to avoid moving obstacles. The proposed hybrid risk-aware decisionmaking architecture is essential for the real-life implementation of AUVs, leading eventually to a real-time adaptive path planning process onboard the AUV

System diagnosis using a bayesian method

Today’s engineering systems have become increasingly more complex. This makes fault diagnosis a more challenging task in industry and therefore a significant amount of research has been undertaken on developing fault diagnostic methodologies. So far there already exist a variety of diagnostic methods, from qualitative to quantitative. However, no methods have considered multi-component degradation when diagnosing faults at the system level. For example, from the point a new aircraft takes off for the first time all of its components start to degrade, and yet in previous studies it is presumed that apart from the faulty component, other components in the system are operating in a healthy state. This thesis makes a contribution through the development of an experimental fuel rig to produce high quality data of multi-component degradation and a probabilistic framework based on the Bayesian method to diagnose faults in a system with considering multi-component degradation. The proposed method is implemented on the fuel rig data which illustrates the applicability of the proposed method and the diagnostic results are compared with the neural network method in order to show the capabilities and imperfections of the proposed method