Abstract
Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.
Highlights
In an automatic manufacturing system, machining tools of computer numerical control (CNC)have always been a crucial factor for machining quality
TD features (TDFs), FD features (FDFs) and TFD features (TFDFs) are extracted by different analysis methods and every channel of sensors can get 10 TDFs, 7 FDFs and top 10 intrinsic mode functions (IMFs)’ TFDFs, and, a total of 162 features are acquired
Taking the data of C1 as an example, the corresponding TFDFs of the force sensor and vibration sensor change trends in Z-direction at the 50th, 150th and 250th cut are shown in Figures 9 and 10
Summary
In an automatic manufacturing system, machining tools of computer numerical control (CNC)have always been a crucial factor for machining quality. In an automatic manufacturing system, machining tools of computer numerical control (CNC). Machining tools wear or breakage may significantly decrease machining quality, increase production costs or even interrupt the running of the manufacturing system [1], and it is estimated 20% of downtime is attributed to tool failures [2]. Online remaining useful life (RUL) prediction and replacement of machining tools in time are urgently needed to assure machining quality and system reliability [3]. A huge amount of research work on RUL prediction of machining tools or equipment has been done over the last decade. RUL prediction methods are divided into main three kinds, which are experience-based models, physics-based models and data-based methods [4,5]. Fuzzy logic methods and expert systems are two typical experience-based methods.
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