Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

Failure Prognosis and Applications—A Survey of Recent Literature

Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when. In this article, a novel deep neural network named convolution-based long short-term memory (CLSTM) network is proposed to predict the RUL of rotating machineries mining the in situ vibration data. Different from previous research that simply connects a convolutional neural network (CNN) to a long short-term memory (LSTM) network serially, the proposed network conducts convolutional operation on both the input-to-state and state-to-state transitions of the LSTM, which contains both time–frequency and temporal information of signals, not only preserving the advantages of LSTM, but also incorporating time–frequency features. The convolutional structure in the LSTM has the ability to capture long-term dependencies and extract features from the time–frequency domain at the same time. By stacking the multiple CLSTM layer-by-layer and forming an encoding-forecasting architecture, the deep learning model is established for RUL prediction in this article. Run-to-failure tests on bearings are conducted, and vibration responses are collected. Using the proposed algorithm, RUL is predicted, and as a comparison, the performance from other methods, including deep CNNs and deep LSTM, is evaluated using the same dataset. The comparative study indicates that the proposed CLSTM network outperforms the current deep learning algorithms in URL prediction and system prognosis with respect to better accuracy and computation efficiency.

Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

Remaining useful life prediction based on health index similarity

Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study

On The Art Of Cutting Metals.pdf

Accurate remaining useful life prediction is meaningful for cutting tool usability evaluation. Over the years, experience-based models, data-driven models, and physics-based models have been used individually to predict cutting tool remaining useful lives. In order to improve prediction performances, different prognostics models can be combined to leverage their advantages. In this paper, a hybrid cutting tool remaining useful life prediction approach is proposed by combining a data-driven model and a physics-based model. By using force, vibration and acoustic emission signals, the data-driven model monitors cutting tool wear conditions based on empirical mode decomposition and back propagation neural network. On the basis of the Wiener process, the physics-based model builds a cutting tool condition degradation model to predict cutting tool remaining useful lives. Experimental study verifies the approach's effectiveness, accuracy, and robustness. Then, cutting tool remaining useful lives can be predicted more accurately during the machining process.

A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process

Experimental and numerical investigation of the progressive collapse of precast reinforced concrete frame substructures with wet connections

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.

An approach to in-process surface texture condition monitoring

Experimental and numerical investigation of dynamic progressive collapse of reinforced concrete beam-column assemblies under a middle-column removal scenario

Rapid urban development has changed urban substrate conditions, greatly affecting urban ecology and heating urban environment. Mitigating urban temperature rises by optimizing urban morphology is considered a promising approach; most studies ignore spatial and temporal heterogeneity. This study analyzes how plot spatial form influences urban thermal environment in the main Nanjing area from 2001, 2006, 2011, 2016, and 2021, based on geographically weighted regression models (spatio-temporal- and multi-scale). Results show that: 1. The formation of geothermal heat islands matches the direction of urban expansion, mainly due to changes in land substrate; 2. the spatio-temporal model performs best, indicating that urban morphology and surface thermal environment have obvious spatio-temporal heterogeneity; obvious scale differences exist in each index influencing the heat island effect; and 3. floor area ratio (FAR) and building density (BD) negatively and positively correlate with surface thermal conditions, with gradually increasing effect, respectively. Normalized difference vegetation index (NDVI) and distance from the nearest water body (Dis_W) negatively and positively correlate with surface thermal conditions separately; good ecological infrastructure reduces surface temperatures but shows a gradually weakening effect. Proximity to roads is associated with warmer thermal environment. This study elucidates how urban form influences surface thermal environments and suggests measures to reduce surface temperatures in the main urban Nanjing area.

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Zidong Zhao

Papers

A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process

Experimental and numerical investigation of the progressive collapse of precast reinforced concrete frame substructures with wet connections

An approach to in-process surface texture condition monitoring

Experimental and numerical investigation of dynamic progressive collapse of reinforced concrete beam-column assemblies under a middle-column removal scenario

How Plot Spatial Morphology Drives Surface Thermal Environment: A Spatial and Temporal Analysis of Nanjing Main City