Engineering

Reliability engineering

Aerospace engineering

Downtime

Principal component analysis

Time series

Probabilistic logic

Operating system

Data mining

Computer science

Condition monitoring

Geology

Paleontology

Mechanical engineering

Data pre-processing

Autoregressive integrated moving average

Artificial intelligence

Series (stratigraphy)

Turbine

Machine learning

Electrical engineering

Preprocessor

A running compressor generates a huge amount of data every day. Some of the data contains useful information that allows the user to determine in advance whether the machine will fail or not. Forecasting in advance cannot only avoid the huge loss caused by unnecessary downtime but also ensure the safety of staff. The main tasks of compressor condition monitoring include data acquisition, data preprocessing, data analysis, alarm module, response strategy analysis, etc. Prediction is one of the tasks of condition monitoring. This paper mainly studies the performance of different time prediction models on the running data produced by a centrifugal compressor. In the data preprocessing part, this paper uses Bayesian wavelet denoising and probabilistic principal component analysis methods proposed by the research group to remove the useless parts in the data. In the part of time series model, the prediction performance of LSTM, GRU and ARIMA on the same dataset is compared. The comparative results indicate that since the dataset is non-stationary, the LSTM and GRU outperform the statistical ARIMA model.

Investigation of Time-series Prediction for Turbine Machinery Condition Monitoring