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Building materials machinery equipment play an important role in the production of cement, brick and tile, glass and other building materials, which are high energy consumption industries. Due to advanced sensors, continuous improvement of signal acquisition technologies and increasing data storage space, a large amount of data can be used by scholars, which makes data-based fault diagnosis gradually studied by more and more scholars. With increasing amount of data, new challenges are as follows: there is very little data that can really be used; the research on compound fault diagnosis and weak fault diagnosis is still not mature; the diagnosis accuracy of variable speed components is low. These problems restrict the further development of fault diagnosis. In this paper, the characteristics of fault diagnosis of building materials equipment are first expounded, the principles and characteristics of main building materials equipment, signal classification, sensor selection and error correction are briefly introduced, then the research status are discussed, the existing difficulties and challenges are summarized, and the potential development directions and trends in this field are given. • Equipment fault diagnosis in high energy consumption industries in China is reviewed. • Common signal types, measurement points, sensor selection and error correction are reviewed. • Current research of equipment fault diagnosis is summarized. • Difficulties and challenges in mechanical fault diagnosis is analyzed. [ABSTRACT FROM AUTHOR]

• A novel feature index named improved manhattan distance is proposed. • Vibration signal is transmitted as SDP image. • Testing experiment based on practical data is carried out to verify the effectiveness. The vibration signal of rolling bearing is typical non-stationary signal, it is difficult for traditional time–frequency analysis technology to apply. Therefore, a novel fault diagnosis approach based on improved manhattan distance in Symmetrized Dot Pattern (SDP) image is proposed. In this method, the sample vibration signal data is divided as 10 equal parts on average, and empirical mode decomposition (EMD) is used to decompose the equally-divided signals into several Intrinsic Mode Function (IMF) components. The first five IMF components are adopted to transform into five symmetrical snowflake image in polar coordinates, after denoising corresponding feature matrices are calculated, and the mean feature matrix and the maximal characteristic root may be obtained. In this way the improved manhattan distance between the local matrix of each IMF components and corresponding mean matrix is extracted. Different vibration signal of rolling bearing is classified according to this improved manhattan distance. Finally, an experimental experiment is carried out to verify the feasibility and effectiveness of this novel fault diagnosis approach. [ABSTRACT FROM AUTHOR]