With the progress of science and technology, as the power source of large rotating machinery, diesel engines need continuous innovation. Because it is a large workload to evaluate the performance of a diesel engine directly through the structural parameters of the engine, researchers have always analyzed the performance of diesel engines by studying vibration signals [
1,
2]. Owing to the non-stationary and nonlinear characteristics of the vibration signal of a diesel engine in rotating machinery [
3,
4], it is difficult to predict it effectively. Therefore, methods to predict the vibration signal of the diesel engine effectively have received extensive attention.
Traditional vibration analysis methods mainly include time–domain analysis, such as extracting the mean amplitude, kurtosis, and other indicators of the signal, but it is difficult to reveal the frequency composition of the signal, the size of each frequency component, and other internal information [
5]. Forms of frequency domain analysis include Fourier transform (FT) [
6,
7]; time–frequency analysis, such as short-time Fourier transform (STFT) [
8], wavelet transform (WT) [
9,
10], etc. Compared with other methods, empirical mode decomposition (EMD) [
11,
12] does not need signal preprocessing in advance; it can decompose signals adaptively layer by layer. The results of this decomposition are finite intrinsic mode functions (IMFs), and it is unnecessary to obtain the prior knowledge of signals during this decomposition process. However, the EMD method is an empirical method, which lacks strong and strict theoretical support and still has many mathematical problems to be solved [
13]. The EMD method itself is still not perfect; there are still some problems, such as mode mixing [
14,
15], endpoint effect [
16,
17], determination of termination criteria [
18,
19], and EMD can only be used for the analysis of one-dimensional real signals. Tang et al. [
20] proposed a method to eliminate mode aliasing in EMD based on improved blind source separation technology. V. G. Kurbatskii et al. [
21] proposed a two-stage adaptive approach for time series forecasting. The efficiency of the developed approach was displayed in a real-time series in the electric power problem of forecasting the sharply variable implementations of active power flows. Hu et al. [
22] proposed a back-projection method to deal with mode aliasing in EMD based on the assumption that every inherent mode function should be locally orthogonal. Hu N Q et al. [
23] used EMD and a deep convolutional neural network for fault diagnosis in a planetary gearbox. V. G. Kurbatskii et al. [
24] proposed a modification of the adaptive approach to time series forecasting. A hybrid genetic algorithm training for an artificial neural network and a regression model based on a support-vector machine were established to verify the effectiveness of the method. Xu K et al. [
25] proposed a rolling bearing fault diagnosis method based on EMD and a support-vector machine. Tim Leung et al. [
26] proposed the method of complementary ensemble empirical mode decomposition (CEEMD) and the Hilbert–Huang transform (HHT) for analyzing nonstationary financial time series. Using a series of examples of empirical financial data, they verified how HHT features enhanced machine learning models in terms of predictive performance. Manohar Mishra et al. [
27] studied the detection of power system voltage sag causes (VSCs), and the interference-balanced voltage signals extracted from relay points were extracted by the signal processing algorithm. Then, VSCs were identified using the input to an extreme learning machine (ELM). To verify the accuracy of the proposed method, ELM performance was compared with manual neural network (ANN), K-nearest Neighbor (KNN), and support-vector machine (SVM) classifiers to verify its validity.
In this paper, a combined algorithm based on improved EMD and ELM [
28,
29] was proposed to predict surface vibration signals for the first time, taking advantage of EMD’s good processing of densely distributed signals and ELM’s ability to accurately describe transient parameter characteristics of non-stationary signals. The original signal is processed by the EMD algorithm to reduce the complexity of the time series. The improved EMD algorithm performs midpoint fitting for the interval to better solve the error caused by interpolation. Therefore, the improved EMD algorithm overcomes the phenomenon of EMD mode aliasing and is robust to noise. Finally, compared with the EMD-ELM combined algorithm, the effectiveness of the IEMD-ELM combined algorithm is verified. All experiments were performed in a diesel engine laboratory.