Monitoring of a Dynamic System Based on Autoencoders
Authors: Aomar Osmani, Massinissa Hamidi, Salah Bouhouche
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results, including hyper-parameter optimization on large real data and domain expert analysis, show that our proposed solution gives promising results. In this section, we evaluate our approach on a real industrial application dataset and demonstrate how it can reliably detect abnormal behavior of such industrial equipment. |
| Researcher Affiliation | Collaboration | 1Laboratoire LIPN-UMR CNRS 7030, PRES Sorbone Paris Cit e, France 2Industrial Technologies Research Center, CRTI-DTSI, Algiers, Algeria |
| Pseudocode | Yes | Algorithm 1 Continuous learning model |
| Open Source Code | Yes | Code to reproduce experiments is publicly available 1. 1https://www.github.com/hamidimassinissa/vibration-sae |
| Open Datasets | No | The paper states, 'Data were collected from a set of 10 sensors that continuously monitor a 102J turbo-compressor operating in a real application.' While it describes the dataset, it does not provide any concrete access information (link, DOI, specific repository, or formal citation for a public dataset) for the dataset used. |
| Dataset Splits | No | The paper mentions 'nominal training period ζ' and 'nominal control period η' in Algorithm 1 and 'nominal training periods ζ {200, 500, 1000, 1500, 2000}' in experiments, but it does not provide explicit details on standard train/validation/test splits using percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or specific computer specifications) used for running the experiments. |
| Software Dependencies | No | The paper states: 'All of our experiments were implemented using py Torch framework [Paszke et al., 2017]'. While PyTorch is mentioned, a specific version number for the framework or any other software dependencies is not provided. |
| Experiment Setup | Yes | The paper provides several experimental setup details, including: 'minibatches of size bs', 'Adam algorithm', 'learning-rate lr and weight-decay d are optimized using the Bayesian optimization procedure', 'gradient clipping at 0.25', 'dropout is applied'. Also, Table 2 'summarizes the hyper-parameters being optimized along with their respective bounds and pairwise marginal importance'. |