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"With the rapid development of AI technology in recent years, there have been\nmany studies with deep learning models in soft sensing area. However, the\nmodels have become more complex, yet, the data sets remain limited: researchers\nare fitting million-parameter models with hundreds of data samples, which is\ninsufficient to exercise the effectiveness of their models and thus often fail\nto perform when implemented in industrial applications. To solve this\nlong-lasting problem, we are providing large scale, high dimensional time\nseries manufacturing sensor data from Seagate Technology to the public. We\ndemonstrate the challenges and effectiveness of modeling industrial big data by\na Soft Sensing Transformer model on these data sets. Transformer is used\nbecause, it has outperformed state-of-the-art techniques in Natural Language\nProcessing, and since then has also performed well in the direct application to\ncomputer vision without introduction of image-specific inductive biases. We\nobserve the similarity of a sentence structure to the sensor readings and\nprocess the multi-variable sensor readings in a time series in a similar manner\nof sentences in natural language. The high-dimensional time-series data is\nformatted into the same shape of embedded sentences and fed into the\ntransformer model. The results show that transformer model outperforms the\nbenchmark models in soft sensing field based on auto-encoder and long\nshort-term memory (LSTM) models. To the best of our knowledge, we are the first\nteam in academia or industry to benchmark the performance of original\ntransformer model with large-scale numerical soft sensing data."
<https://cdnjs.deepai.org/profile/xiaoye-qian>