Correct and timely fault diagnosis is of great importance for enhancing the safety and reliability of modern chemical industrial processes. With the arrival of the big data era, data-driven fault detection and diagnosis (FDD) methods offer enormous potential for complex chemical processes. Deep learning-based data-driven FDD methods, which extract features from raw data using an artificial neural network (ANN), are attracting widespread attention. Among various types of neural networks, recurrent neural network (RNN) performs excellently when dealing with time-series data. However, a regular unidirectional RNN proceeds only in the positive time direction, resulting in insufficient feature extraction and inferior fault diagnosis performance. In this study, a bidirectional RNN (BiRNN) was employed to construct FDD models with sophisticated RNN cells. When applied to the benchmark Tennessee Eastman process, BiRNN-based FDD models exhibited a dramatically impressive performance, demonstrating the effectiveness of implementing BiRNN in chemical process fault diagnosis.