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Jianlong Li

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EAAI Journal 2025 Journal Article

A time and frequency convolutional Autoencoder for anomaly detection in industrial robots based on inertial measurement unit error calibration

  • Jianlong Li
  • Xiaoqin Liu
  • Xing Wu
  • Dongxiao Wang
  • Kai Xu
  • Yashan Li

In the realm of industrial robots, ensuring operational reliability and Long-Term Autonomy hinges on the accurate detection of anomalies. However, this sample difference due to noise, joint random errors and sensor errors increases the challenge of robot anomaly detection. To address this problem, an unsupervised deep learning method based on inertial measurement unit (IMU) error calibration is proposed. Firstly, the attitude signals acquired by the IMU from the end of the robot were calibrated using Kalman filtering. The three dimensional (3D) free acceleration was corrected based on the calibrated attitude signal and the calibrated 3D free acceleration signal was used as a signal sample. Secondly, a time and frequency convolutional autoencoder model (TFCAE) is proposed. And the distribution of the different component signals is fitted by stacking multiple encoder modules and 3D-TFCAE is used for 3D free acceleration signal reconstruction model. Then, the error sphere radius is calculated based on the reconstruction error of the 3D free acceleration signal. And the error sphere radius is used as the anomaly detection threshold to realize the robust detection of different types of anomalies. The model was evaluated on a constructed anomaly dataset. This study contributes an innovative 3D-TFCAE architecture, integrating Kalman filtering with time-frequency feature fusion, markedly enhancing anomaly detection in complex signal environments. Experimental findings reveal that 3D-TFCAE significantly outperforms 18 baseline models, improving detection accuracy by about 20 %–40 %, offering an effective solution for high-precision anomaly detection in industrial robots. The code for this project is available at https: //github. com/LJlong977/3DTFCAE.

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NeurIPS Conference 2025 Conference Paper

Functional Complexity-adaptive Temporal Tensor Decomposition

  • Panqi Chen
  • Lei Cheng
  • Jianlong Li
  • Weichang Li
  • Weiqing Liu
  • Jiang Bian
  • Shikai Fang

Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they still struggle with general tensor data with continuous indexes not only in the temporal mode but also in other modes, such as spatial coordinates in climate data. Moreover, the challenge of self-adapting model complexity is largely unexplored in functional temporal tensor models, with existing methods being inapplicable in this setting. To address these limitations, we propose functional Complexity-Adaptive Temporal Tensor dEcomposition (Catte). Our approach encodes continuous spatial indexes as learnable Fourier features and employs neural ODEs in latent space to learn the temporal trajectories of factors. To enable automatic adaptation of model complexity, we introduce a sparsity-inducing prior over the factor trajectories. We develop an efficient variational inference scheme with an analytical evidence lower bound, enabling sampling-free optimization. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that Catte not only reveals the underlying ranks of functional temporal tensors but also significantly outperforms existing methods in prediction performance and robustness against noise.

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