Technologies like self-driving cars and cleaning robots are emerging as mainstream technologies. These technologies make use of cognitive recognition. Non-negative matrix factorization (NMF) is one such technique that is popularly used for computer vision and hidden pattern recognition. NMF is prone to noises because it assumes the image signal to be linearly reconstructed. This work proposes an algorithm to increase the effectiveness of NMF and reduces the data to lower dimensions and add informational presentation which improves the clustering results of NMF. The effectiveness of the proposed model is measured by comparing them on attributes namely accuracy, homogeneity, and inertia. Some of the models that we used include K-means, PCA+K-means, NMF+K-means, Autoencoder + PCA + K-means. Our proposed model is observed to be the most effective for clustering denoised data. The algorithm also takes care of the different fault detections and gives a non-linear method based on NMF. Here, we first used autoencoders which are given input data to learn the non-linear mapping so that it can be transformed into high-dimensional space. By using the decomposition rule, we divided our feature space into two parts: The first one comprises the encoder, NMF, and decoder. This method of DNMF is a non-linear framework that can further be extended to other linear methods. The proposed method also expands the NMF's application range as it can also accept non-negative input.