A quality-related fault monitoring method of multi-phase batch process based on multiway weighted elastic network is proposed. Firstly, to make the phase division for batch process more accurately, an improved affinity propagation clustering is developed. Secondly, a multiway weighted elastic network is developed in each phase. On the one hand, quality-related subspace and quality-unrelated subspace are construct in each phase to achieve dual monitoring of process fault and quality anomalies. On the other hand, kernel density estimation is used to measure the contribution of each element in each subspace to the fault. According to the difference of the contribution, different weight is assigned to enhance the fault features and eliminate noise. Finally, support vector data description is used to establish monitoring indexes in both quality-related subspace and quality-unrelated subspace. Compared with traditional methods, the superiority of the proposed method have been verified by monitoring the penicillin fermentation process.