A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper introduces a hybrid GPR model for more interpretable RUL prediction in aeroengine.
CBMs improve interpretability in RUL prediction for aircraft engines.
This paper proposes a multi-head attention model for predicting RUL in IIoT environments.
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
ATS2S model predicts RUL of industrial equipment using attention mechanism.
Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.
Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios wher…
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which lever…
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
Paper tackles RUL prediction with scarce data using indirect supervision.
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (R…
In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to …
Mix-up domain adaptation improves dynamic RUL predictions across various conditions.
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model…
Proposes ECLSTM for more accurate RUL estimation from time series data.
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to …
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates t…
With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers su…
Quantum Annealing Enhanced Reinforcement Learning for Accurate RUL Prediction
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical component…
The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very simi…
The U.S. water distribution system contains thousands of miles of pipes constructed from different materials, and of various sizes, and age. These pipes suffer from physical, environmental, structural and operational stresses, causing deterioration which eventually leads to their failure. Pipe deterioration results in …
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this wa…
Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML…
Unified model predicts multi-mode failure with multi-sensor data.
In the last decade, deep learning (DL) has outperformed model-based and statistical approaches in predicting the remaining useful life (RUL) of machinery in the context of condition-based maintenance. One of the major drawbacks of DL is that it heavily depends on a large amount of labeled data, which are typically expe…
This study evaluates uncertainty quantification methods for deep learning in predictive maintenance.
Deep learning improves oilfield equipment maintenance and reduces downtime.
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure…
Graph neural networks improve equipment health monitoring from multisensor data.
Bayesian deep learning improves maintenance planning uncertainty quantification.
New definition of interpretability makes model design more actionable.
New method improves neural network interpretability against adversarial attacks.
VALC provides concept-level interpretations of FLMs, overcoming word-level limitations.
New framework improves interpretability of trainable prompts.
Interpreting machine learning models helps understand adversarial attacks and defenses.
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide…
Model-agnostic interpretation methods can mislead if not used carefully.
Study on trade-offs between accuracy and interpretability in machine learning.
Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and s…
New framework learns interpretable rule ensembles without sacrificing accuracy.
Study finds machine learning interpretations are often unstable and unreliable.
Meta-learning approach to learn interpretable models from human feedback.
A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and prior to favor interpretable models. Fundamentally, however, interpretability is about users' preferences, not the data generation mechanis…
We create interpretable word embeddings through sparse coding.
Proposes a model to interpret complex ML algorithms.