53 research outputs found
Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
Accurate energy consumption prediction is crucial for optimizing the
operation of electric commercial heavy-duty vehicles, e.g., route planning for
charging. Moreover, understanding why certain predictions are cast is paramount
for such a predictive model to gain user trust and be deployed in practice.
Since commercial vehicles operate differently as transportation tasks, ambient,
and drivers vary, a heterogeneous population is expected when building an AI
system for forecasting energy consumption. The dependencies between the input
features and the target values are expected to also differ across
sub-populations. One well-known example of such a statistical phenomenon is the
Simpson paradox. In this paper, we illustrate that such a setting poses a
challenge for existing XAI methods that produce global feature statistics, e.g.
LIME or SHAP, causing them to yield misleading results. We demonstrate a
potential solution by training multiple regression models on subsets of data.
It not only leads to superior regression performance but also more relevant and
consistent LIME explanations. Given that the employed groupings correspond to
relevant sub-populations, the associations between the input features and the
target values are consistent within each cluster but different across clusters.
Experiments on both synthetic and real-world datasets show that such splitting
of a complex problem into simpler ones yields better regression performance and
interpretability
RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs
General chat models, like ChatGPT, have attained impressive capability to
resolve a wide range of NLP tasks by tuning Large Language Models (LLMs) with
high-quality instruction data. However, collecting human-written high-quality
data, especially multi-turn dialogues, is expensive and unattainable for most
people. Though previous studies have used powerful LLMs to generate the
dialogues automatically, but they all suffer from generating untruthful
dialogues because of the LLMs hallucination. Therefore, we propose a method
called RefGPT to generate enormous truthful and customized dialogues without
worrying about factual errors caused by the model hallucination. RefGPT solves
the model hallucination in dialogue generation by restricting the LLMs to
leverage the given reference instead of reciting their own knowledge to
generate dialogues. Additionally, RefGPT adds detailed controls on every
utterances to enable highly customization capability, which previous studies
have ignored. On the basis of RefGPT, we also propose two high-quality dialogue
datasets generated by GPT-4, namely RefGPT-Fact and RefGPT-Code. RefGPT-Fact is
100k multi-turn dialogue datasets based on factual knowledge and RefGPT-Code is
76k multi-turn dialogue dataset covering a wide range of coding scenarios. Our
code and datasets are released in https://github.com/ziliwangnlp/RefGP
Explainable Predictive Maintenance
Explainable Artificial Intelligence (XAI) fills the role of a critical
interface fostering interactions between sophisticated intelligent systems and
diverse individuals, including data scientists, domain experts, end-users, and
more. It aids in deciphering the intricate internal mechanisms of ``black box''
Machine Learning (ML), rendering the reasons behind their decisions more
understandable. However, current research in XAI primarily focuses on two
aspects; ways to facilitate user trust, or to debug and refine the ML model.
The majority of it falls short of recognising the diverse types of explanations
needed in broader contexts, as different users and varied application areas
necessitate solutions tailored to their specific needs.
One such domain is Predictive Maintenance (PdM), an exploding area of
research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights
the gap between existing XAI methodologies and the specific requirements for
explanations within industrial applications, particularly the Predictive
Maintenance field. Despite explainability's crucial role, this subject remains
a relatively under-explored area, making this paper a pioneering attempt to
bring relevant challenges to the research community's attention. We provide an
overview of predictive maintenance tasks and accentuate the need and varying
purposes for corresponding explanations. We then list and describe XAI
techniques commonly employed in the literature, discussing their suitability
for PdM tasks. Finally, to make the ideas and claims more concrete, we
demonstrate XAI applied in four specific industrial use cases: commercial
vehicles, metro trains, steel plants, and wind farms, spotlighting areas
requiring further research.Comment: 51 pages, 9 figure
Wisdom of the Crowd for Fault Detection and Prognosis
Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers.
A Self-Organized Fault Detection Method for Vehicle Fleets
A fleet of commercial heavy-duty vehicles is a very interesting application arena for fault detection and predictive maintenance. With a highly digitized electronic system and hundreds of sensors mounted on-board a modern bus, a huge amount of data is generated from daily operations. This thesis and appended papers present a study of an autonomous framework for fault detection, using the data gathered from the regular operation of vehicles. We employed an unsupervised deviation detection method, called Consensus Self-Organising Models (COSMO), which is based on the concept of ‘wisdom of the crowd’. It assumes that the majority of the group is ‘healthy’; by comparing individual units within the group, deviations from the majority can be considered as potentially ‘faulty’. Information regarding detected anomalies can be utilized to prevent unplanned stops. This thesis demonstrates how knowledge useful for detecting faults and predicting failures can be autonomously generated based on the COSMO method, using different generic data representations. The case study in this work focuses on vehicle air system problems of a commercial fleet of city buses. We propose an approach to evaluate the COSMO method and show that it is capable of detecting various faults and indicates upcoming air compressor failures. A comparison of the proposed method with an expert knowledge based system shows that both methods perform equally well. The thesis also analyses the usage and potential benefits of using the Echo State Network as a generic data representation for the COSMO method and demonstrates the capability of Echo State Network to capture interesting characteristics in detecting different types of faults.In4Uptim
Exploration and Mapping of Warehouses Using QuadrotorHelicopters
The goal of this thesis work is mapping of warehouse infrastructure using AR.Drone, aquadro helicopter equipped with a wide angle camera as its main sensor for mapping.Parallel Tracking and Mapping algorithm is employed for localization and creationof 3D point cloud of corner feature in the environment. Our approach for mapping was adata fusion algorithm that combines point cloud data from PTAM with estimated pillarpositions extracted by multi-stage image analysis algorithm.The methods implemented in ROS and Matlab has been successfully tested in real warehouseenvironment. The system is capable of creating an accurate map if some factorsare controlled. Details of results and analysis of methods are provided.Automatic Inventory and Mapping of Stoc
Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure. In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.ISBN: 978-1-61499-588-3 (print) | 978-1-61499-589-0 (online)Editor: SÅ‚awomir NowaczykIn4Uptim
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