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做网站的知名公司,填报wordpress模板,小程序免费制作平台登录,做框图的网站✨ 本团队擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导#xff0c;毕业论文、期刊论文经验交流。 ✅ 专业定制毕设、代码 ✅ 成品或定制#xff0c;查看文章底部微信二维码 (1) 基于麻雀搜索算法优化变分模态提取的轴承微弱故障特征增强方法 滚动轴承…✨ 本团队擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导毕业论文、期刊论文经验交流。✅ 专业定制毕设、代码✅ 成品或定制查看文章底部微信二维码(1) 基于麻雀搜索算法优化变分模态提取的轴承微弱故障特征增强方法滚动轴承在实际工业环境中运行时其振动信号往往受到强烈的背景噪声干扰包括其他机械部件的振动传递、电磁干扰和测量系统本身的噪声等。当轴承处于早期故障阶段时由局部缺陷引起的冲击振动响应能量微弱信噪比极低故障特征容易被噪声掩盖而难以有效提取。变分模态提取是一种基于变分优化理论的单分量信号提取方法能够从复杂混合信号中分离出感兴趣的模态分量。该方法通过构造包含带宽约束的变分问题迭代求解使目标模态的频带集中度最大化。然而变分模态提取的性能对惩罚因子和初始中心频率两个关键参数较为敏感参数选择不当会导致提取结果偏离真实故障特征。本研究引入麻雀搜索算法对变分模态提取的参数进行自适应优化。麻雀搜索算法模拟麻雀群体的觅食行为通过发现者、跟随者和警戒者三种角色的协同配合实现全局寻优。以提取模态的能量特征指标为适应度函数引导算法搜索能够最大化故障冲击能量的最优参数组合。完成变分模态提取后为进一步抑制残余噪声并增强周期性冲击特征采用稀疏最大谐波噪声比解卷积算法对提取的模态分量进行后处理。该解卷积方法通过最大化输出信号的谐波噪声比来设计最优滤波器能够有效恢复被传递路径平滑效应模糊的原始冲击序列。实验验证表明所提方法在强噪声背景下能够清晰提取轴承内圈、外圈和滚动体故障的特征频率成分。(2) 基于核主成分分析与改进麻雀优化核极限学习机的故障分类识别方法在完成故障特征提取后需要建立有效的分类模型实现不同故障类型的自动识别。滚动轴承故障振动信号具有强烈的非线性和非平稳特性提取的原始特征维度往往较高且存在冗余信息直接输入分类器可能导致计算复杂度过高和过拟合问题。核主成分分析是一种基于核技巧的非线性降维方法通过隐式地将原始数据映射到高维特征空间再进行主成分分析能够有效处理数据中的非线性结构关系。本研究首先采用核主成分分析对高维故障特征进行降维处理保留累计贡献率超过设定阈值的主成分作为分类器的输入既降低了数据维度减少计算量又保留了故障判别的关键信息。在分类器选择上采用核极限学习机作为基础模型。核极限学习机结合了极限学习机的快速训练特性和核方法的非线性映射能力只需设置核函数参数和正则化系数即可完成模型训练。然而这两个超参数的选择对分类性能影响显著本研究提出一种改进的麻雀搜索算法对其进行优化。改进策略包括采用帐篷混沌映射初始化种群以增加搜索空间覆盖度以及在迭代后期引入高斯变异算子帮助算法跳出局部最优。以交叉验证准确率为优化目标搜索使分类性能最优的参数组合。在多组轴承故障数据集上的测试结果表明所提方法相比于传统支持向量机和标准极限学习机具有更高的诊断准确率和更好的泛化能力。(3) 基于鲸鱼优化变分模态分解与粒子群支持向量机的综合诊断框架考虑到不同优化算法和分类模型各有其优势和适用场景本研究进一步构建了一种融合多种智能优化技术的综合故障诊断框架以适应更加复杂多变的工业应用需求。在信号分解环节采用变分模态分解算法将原始振动信号分解为若干个本征模态分量。变分模态分解的关键参数包括分解层数和惩罚因子分解层数决定了信号被分离为多少个模态惩罚因子则影响各模态的带宽约束强度。本研究引入鲸鱼优化算法对这两个参数进行联合寻优鲸鱼优化算法模拟座头鲸的螺旋捕食行为具有收敛速度快和全局搜索能力强的特点。以分解结果的包络熵最小化为优化目标自动确定最优的分解参数配置。对分解得到的各本征模态分量计算样本熵作为故障特征指标样本熵能够量化信号的复杂度和规则性不同故障状态对应不同的样本熵数值模式。在分类识别环节采用支持向量机作为故障分类器利用粒子群优化算法对支持向量机的核函数参数和惩罚参数进行寻优。粒子群算法通过模拟鸟群觅食行为实现参数空间的高效搜索以分类准确率为适应度函数指导参数优化方向。import numpy as np from scipy.signal import hilbert from scipy.fft import fft, fftfreq from sklearn.svm import SVC from sklearn.decomposition import KernelPCA from sklearn.model_selection import cross_val_score from sklearn.preprocessing import StandardScaler class SSAOptimizer: def __init__(self, objective, dim, bounds, pop_size30, max_iter100): self.objective objective self.dim dim self.bounds np.array(bounds) self.pop_size pop_size self.max_iter max_iter def optimize(self): lb, ub self.bounds[:, 0], self.bounds[:, 1] population lb (ub - lb) * np.random.rand(self.pop_size, self.dim) fitness np.array([self.objective(ind) for ind in population]) sorted_idx np.argsort(fitness) best_solution population[sorted_idx[0]].copy() best_fitness fitness[sorted_idx[0]] for t in range(self.max_iter): n_producers int(0.2 * self.pop_size) n_scouts int(0.1 * self.pop_size) R2 np.random.rand() for i in sorted_idx[:n_producers]: if R2 0.8: population[i] population[i] * np.exp(-i / (np.random.rand() * self.max_iter 1e-10)) else: population[i] population[i] np.random.randn(self.dim) worst_idx sorted_idx[-1] for i in sorted_idx[n_producers:-n_scouts]: if i self.pop_size / 2: population[i] np.random.randn(self.dim) * np.exp( (population[worst_idx] - population[i]) / (i**2 1e-10)) else: A np.random.randint(0, 2, self.dim) * 2 - 1 population[i] best_solution np.abs(population[i] - best_solution) * A for i in sorted_idx[-n_scouts:]: if fitness[i] best_fitness: population[i] best_solution np.random.randn(self.dim) * \ np.abs(population[i] - best_solution) population np.clip(population, lb, ub) fitness np.array([self.objective(ind) for ind in population]) sorted_idx np.argsort(fitness) if fitness[sorted_idx[0]] best_fitness: best_fitness fitness[sorted_idx[0]] best_solution population[sorted_idx[0]].copy() return best_solution, best_fitness class VMEExtractor: def __init__(self): self.extracted_mode None self.center_frequency None def extract(self, signal, alpha, omega_init, tau0, tol1e-7, max_iter500): N len(signal) freqs np.fft.fftfreq(N) f_hat np.fft.fft(signal) u_hat np.zeros(N, dtypecomplex) omega omega_init for _ in range(max_iter): u_hat_old u_hat.copy() numerator f_hat denominator 1 2 * alpha * (freqs - omega)**2 u_hat numerator / denominator if np.sum(np.abs(u_hat)**2) 0: omega np.abs(np.sum(freqs * np.abs(u_hat)**2) / np.sum(np.abs(u_hat)**2)) omega np.clip(omega, 0, 0.5) if np.sum(np.abs(u_hat - u_hat_old)**2) / (np.sum(np.abs(u_hat_old)**2) 1e-10) tol: break self.extracted_mode np.real(np.fft.ifft(u_hat)) self.center_frequency omega return self.extracted_mode def compute_energy_index(self, mode): analytic hilbert(mode) envelope np.abs(analytic) return np.sum(envelope**2) class SMHDDeconvolution: def __init__(self, filter_length100, period_samples50): self.L filter_length self.T period_samples def deconvolve(self, signal, num_harmonics5): N len(signal) X np.zeros((N - self.L 1, self.L)) for i in range(N - self.L 1): X[i] signal[i:i self.L] target np.zeros(N - self.L 1) for h in range(1, num_harmonics 1): positions np.arange(0, N - self.L 1, self.T // h) positions positions[positions len(target)] target[positions] 1.0 / h target / np.max(target) XtX X.T X 0.01 * np.eye(self.L) Xty X.T target f np.linalg.solve(XtX, Xty) return np.convolve(signal, f, modesame) class SSAOptimizedVME: def __init__(self): self.vme VMEExtractor() self.smhd SMHDDeconvolution() def extract_fault_feature(self, signal, fs): def objective(params): alpha, omega_init params mode self.vme.extract(signal, alpha, omega_init) energy self.vme.compute_energy_index(mode) return -energy optimizer SSAOptimizer(objective, dim2, bounds[(100, 5000), (0.01, 0.49)], pop_size20, max_iter50) best_params, _ optimizer.optimize() alpha_opt, omega_opt best_params extracted_mode self.vme.extract(signal, alpha_opt, omega_opt) fault_freq_hz omega_opt * fs period_samples int(fs / fault_freq_hz) if fault_freq_hz 0 else 50 self.smhd.T max(10, period_samples) enhanced_signal self.smhd.deconvolve(extracted_mode) return enhanced_signal, fault_freq_hz class ImprovedSSAOptimizer(SSAOptimizer): def __init__(self, objective, dim, bounds, pop_size30, max_iter100): super().__init__(objective, dim, bounds, pop_size, max_iter) def tent_chaos_init(self): lb, ub self.bounds[:, 0], self.bounds[:, 1] population np.zeros((self.pop_size, self.dim)) x np.random.rand(self.dim) for i in range(self.pop_size): x np.where(x 0.5, 2 * x, 2 * (1 - x)) population[i] lb x * (ub - lb) return population def gaussian_mutation(self, individual, sigma0.1): lb, ub self.bounds[:, 0], self.bounds[:, 1] mutated individual sigma * np.random.randn(self.dim) * (ub - lb) return np.clip(mutated, lb, ub) def optimize(self): population self.tent_chaos_init() fitness np.array([self.objective(ind) for ind in population]) sorted_idx np.argsort(fitness) best_solution population[sorted_idx[0]].copy() best_fitness fitness[sorted_idx[0]] for t in range(self.max_iter): n_producers int(0.2 * self.pop_size) for i in sorted_idx[:n_producers]: if np.random.rand() 0.8: population[i] population[i] * np.exp(-i / (np.random.rand() * self.max_iter 1e-10)) else: population[i] population[i] np.random.randn(self.dim) for i in sorted_idx[n_producers:]: A np.random.randint(0, 2, self.dim) * 2 - 1 population[i] best_solution np.abs(population[i] - best_solution) * A if t self.max_iter * 0.7: mutation_idx np.random.choice(self.pop_size, sizeint(0.2 * self.pop_size), replaceFalse) for idx in mutation_idx: population[idx] self.gaussian_mutation(population[idx]) population np.clip(population, self.bounds[:, 0], self.bounds[:, 1]) fitness np.array([self.objective(ind) for ind in population]) sorted_idx np.argsort(fitness) if fitness[sorted_idx[0]] best_fitness: best_fitness fitness[sorted_idx[0]] best_solution population[sorted_idx[0]].copy() return best_solution, best_fitness class KPCAFeatureReducer: def __init__(self, n_components10, kernelrbf, gamma0.1): self.kpca KernelPCA(n_componentsn_components, kernelkernel, gammagamma) self.scaler StandardScaler() def fit_transform(self, features): scaled self.scaler.fit_transform(features) return self.kpca.fit_transform(scaled) def transform(self, features): scaled self.scaler.transform(features) return self.kpca.transform(scaled) class KELMClassifier: def __init__(self, kernelrbf, gamma1.0, C1.0): self.gamma gamma self.C C self.alpha None self.X_train None self.y_train None def rbf_kernel(self, X1, X2): sq_dists np.sum(X1**2, axis1).reshape(-1, 1) \ np.sum(X2**2, axis1) - 2 * X1 X2.T return np.exp(-self.gamma * sq_dists) def fit(self, X, y): self.X_train X self.y_train y n_samples X.shape[0] n_classes len(np.unique(y)) Y np.zeros((n_samples, n_classes)) for i, label in enumerate(y): Y[i, int(label)] 1 K self.rbf_kernel(X, X) self.alpha np.linalg.solve(K np.eye(n_samples) / self.C, Y) def predict(self, X): K self.rbf_kernel(X, self.X_train) output K self.alpha return np.argmax(output, axis1) class CGSSAOptimizedKELM: def __init__(self): self.kpca KPCAFeatureReducer() self.kelm None self.best_params None def optimize_and_train(self, X, y, gamma_range(0.001, 10), C_range(0.1, 100)): X_reduced self.kpca.fit_transform(X) def objective(params): gamma, C params kelm KELMClassifier(gammagamma, CC) scores [] for _ in range(3): idx np.random.permutation(len(y)) train_idx, val_idx idx[:int(0.8*len(y))], idx[int(0.8*len(y)):] kelm.fit(X_reduced[train_idx], y[train_idx]) pred kelm.predict(X_reduced[val_idx]) scores.append(np.mean(pred y[val_idx])) return -np.mean(scores) optimizer ImprovedSSAOptimizer(objective, dim2, bounds[gamma_range, C_range], pop_size20, max_iter50) best_params, _ optimizer.optimize() self.best_params {gamma: best_params[0], C: best_params[1]} self.kelm KELMClassifier(**self.best_params) self.kelm.fit(X_reduced, y) return self.best_params def predict(self, X): X_reduced self.kpca.transform(X) return self.kelm.predict(X_reduced) class BearingFaultDiagnosisSystem: def __init__(self, fs12000): self.fs fs self.feature_extractor SSAOptimizedVME() self.classifier CGSSAOptimizedKELM() def extract_features(self, signals): features [] for signal in signals: enhanced, fault_freq self.feature_extractor.extract_fault_feature(signal, self.fs) spectrum np.abs(fft(enhanced))[:len(enhanced)//2] feat [ np.mean(enhanced), np.std(enhanced), np.max(enhanced), np.sqrt(np.mean(enhanced**2)), np.mean(enhanced**3) / (np.std(enhanced)**3 1e-10), np.mean(enhanced**4) / (np.std(enhanced)**4 1e-10), fault_freq, np.max(spectrum), np.argmax(spectrum) * self.fs / len(enhanced) ] features.append(feat) return np.array(features) def train(self, signals, labels): features self.extract_features(signals) params self.classifier.optimize_and_train(features, labels) return params def diagnose(self, signal): features self.extract_features([signal]) prediction self.classifier.predict(features) fault_types [normal, inner_race, outer_race, ball] return fault_types[prediction[0]]具体问题可以直接沟通