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With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more and more complex, yet, the data sets remain limited: researchers are fitting million-parameter models with hundreds of data samples, which is insufficient to exercise the effectiveness of their models. To solve this long-lasting problem, we are providing large-scale high-dimensional time series manufacturing sensor data from Seagate Technology to the public. We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets. Transformer is used because, it has outperformed state-of-the-art techniques in Natural Language Processing, and since then has also performed well in the direct application to computer vision without introduction of image- specific inductive biases. We observe the similarity of a sentence structure to the sensor readings and process the multi-variable sensor readings in a similar manner of sentences in natural language. The high-dimensional time series data is formatted into the same shape of embedded sentences and fed into the transformer model. The results show that transformer model outperforms the benchmark models in soft sensing field based on auto-encoder and long short-term memory (LSTM). To the best of our knowledge, we are the first team in academia or industry to benchmark the performance of original transformer model with large-scale numerical soft sensing data. Additionally, In contrast to the natural language processing or computer vision tasks where human-level performances are regarded as golden standards, our large-scale soft sensing study is an example that transformer is able to interpret high-dimensional numerical data which is not interpretable by human.

Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word