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wordpress网站怎么建,厦门专业做网站公司,wordpress表格功能,福州网站设计软件✅ 博主简介#xff1a;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。 ✅成品或者定制#xff0c;扫描文章底部微信二维码。 (1) 基于优化共振稀疏分解的轴承微弱故障特征增强方法 滚动轴承在复杂工况条件下运…✅博主简介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。✅成品或者定制扫描文章底部微信二维码。(1) 基于优化共振稀疏分解的轴承微弱故障特征增强方法滚动轴承在复杂工况条件下运行时其振动信号中的故障特征往往十分微弱容易被环境噪声、机械传动干扰和转速波动等因素所掩盖给故障早期检测带来极大困难。共振稀疏信号分解是一种基于信号形态学特征的分离技术能够将原始振动信号分解为高共振分量、低共振分量和残余分量三个部分其中低共振分量包含了与轴承故障冲击相关的瞬态成分而高共振分量则主要是周期性的转动频率成分。然而该方法的分解效果强烈依赖于品质因子等关键超参数的选择不恰当的参数设置可能导致目标故障成分无法被有效分离。针对这一问题本研究引入蜣螂优化算法对共振稀疏分解的超参数进行自动寻优。蜣螂优化算法是一种模拟蜣螂滚球、跳舞和觅食行为的新型群体智能算法具有收敛速度快和全局搜索能力强的特点。以分解后低共振分量的峭度值作为优化目标通过迭代搜索确定能够最大化故障冲击成分峭度的最优参数组合。在完成信号分解后对低共振分量进行子带能量分析利用谱峭度或包络谱能量分布准则筛选出包含主要冲击成分的频带并进行叠加重构。考虑到轴承故障信号本质上是周期性冲击与传递路径卷积的结果进一步采用多点最优最小熵解卷积算法对重构信号进行处理通过反卷积运算增强周期性冲击的尖锐程度并抑制传递函数的平滑效应。经过上述多阶段处理后对增强信号进行包络解调和频谱分析可以清晰地识别出与轴承内圈、外圈或滚动体故障特征频率相对应的谱峰实现微弱故障的有效检测。(2) 基于自适应分解与稀疏贝叶斯学习的复合故障分离诊断方法在实际工业环境中滚动轴承可能同时存在多个故障部位或多种故障类型的复合故障情况不同故障产生的振动响应相互叠加耦合使得各故障成分的特征频率混杂在一起难以分辨。传统的单一故障诊断方法在面对复合故障时往往力不从心容易出现漏诊或误诊。本研究提出一种融合自适应模态分解与稀疏贝叶斯学习的阶段性故障分离策略旨在将复合故障信号中的不同故障成分逐一分离并分别诊断。在信号预处理阶段设计一种基于复合评价指标的自适应模态分解方法该指标综合考虑分解分量的故障敏感度、信噪比和模态正交性等因素。通过可调品质因子的滤波器组对原始信号进行多尺度分解自适应地选择能够最大化复合指标的分解模式并利用互相关系数剔除冗余的模态分量。对筛选后的有效模态分量分别进行包络谱分析采用包络谐波积谱方法估计各分量中可能存在的故障特征频率。包络谐波积谱通过计算包络谱在基频及其整数倍谐波处幅值的乘积来增强周期性成分的辨识度能够在强噪声背景下更准确地估计故障特征频率。将估计得到的特征频率作为稀疏贝叶斯学习的先验知识构建包含各故障频率成分的字典矩阵利用贝叶斯推理框架求解信号在该字典上的稀疏表示系数。稀疏贝叶斯学习能够自动确定系数的稀疏度并给出各频率成分的后验概率分布根据稀疏系数的幅值和置信区间可以判断对应故障成分是否存在以及其严重程度。(3) 基于学习字典与加权稀疏表示的轴承故障分类识别方法面向工业大数据场景下的轴承故障自动分类识别需求本研究提出一种结合字典学习与加权稀疏表示的智能诊断方法。传统的稀疏表示分类方法采用固定的过完备字典对信号进行稀疏编码但固定字典难以充分适应不同工况和不同设备采集的振动信号特征差异。为了提高字典对目标信号的表示能力采用基于奇异值分解的字典学习算法从训练样本中自适应地学习最优字典原子。该算法通过交替优化稀疏编码和字典更新两个步骤使学习得到的字典能够以最少的原子数量精确重构训练信号从而捕捉信号中的本质特征结构。在分类识别阶段针对传统稀疏表示分类中所有样本同等对待而忽视信号局部特征差异的问题设计一种基于时域统计特征的样本加权策略。具体而言计算测试样本与各类训练样本在峭度、偏度、波形因子等时域指标上的相似度将相似度较高的训练样本在稀疏表示中赋予更大的权重。import numpy as np from scipy.signal import hilbert, butter, filtfilt, find_peaks from scipy.fft import fft, ifft, fftfreq from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.preprocessing import normalize class DBOOptimizer: 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]) best_idx np.argmax(fitness) best_solution population[best_idx].copy() best_fitness fitness[best_idx] for t in range(self.max_iter): sorted_idx np.argsort(fitness)[::-1] n_ball_rolling int(0.4 * self.pop_size) for i in sorted_idx[:n_ball_rolling]: alpha 1 - t / self.max_iter k 0.1 * np.random.randn(self.dim) b 0.3 * np.random.randn(self.dim) population[i] population[i] alpha * k * (best_solution - population[i]) b n_breeding int(0.3 * self.pop_size) for i in sorted_idx[n_ball_rolling:n_ball_rolling n_breeding]: local_best population[sorted_idx[np.random.randint(0, n_ball_rolling)]] population[i] population[i] np.random.randn(self.dim) * (local_best - population[i]) for i in sorted_idx[n_ball_rolling n_breeding:]: population[i] lb (ub - lb) * np.random.rand(self.dim) population np.clip(population, lb, ub) fitness np.array([self.objective(ind) for ind in population]) current_best_idx np.argmax(fitness) if fitness[current_best_idx] best_fitness: best_fitness fitness[current_best_idx] best_solution population[current_best_idx].copy() return best_solution, best_fitness class ResonanceSparseSeparator: def __init__(self, signal, fs): self.signal signal self.fs fs def tqwt_decompose(self, Q, r, J): N len(self.signal) X fft(self.signal) subbands [] for j in range(J): alpha 1 - 1 / (Q 1) beta 1 / r low_freq self.fs / 2 * alpha**j * beta high_freq self.fs / 2 * alpha**j freqs fftfreq(N, 1/self.fs) mask (np.abs(freqs) low_freq) (np.abs(freqs) high_freq) subband_fft X * mask subbands.append(np.real(ifft(subband_fft))) return np.array(subbands) def compute_kurtosis(self, signal): mean np.mean(signal) std np.std(signal) if std 1e-10: return 0 return np.mean((signal - mean)**4) / std**4 def optimize_decomposition(self, Q_range(1, 10), r_range(2, 6), J_range(3, 10)): def objective(params): Q, r, J params J int(J) try: subbands self.tqwt_decompose(Q, r, J) low_resonance np.sum(subbands[J//2:], axis0) return self.compute_kurtosis(low_resonance) except: return 0 optimizer DBOOptimizer(objective, dim3, bounds[Q_range, r_range, J_range]) best_params, best_kurtosis optimizer.optimize() return best_params def extract_fault_component(self, Q, r, J): subbands self.tqwt_decompose(Q, r, int(J)) kurtosis_values [self.compute_kurtosis(sb) for sb in subbands] threshold np.mean(kurtosis_values) np.std(kurtosis_values) selected_idx [i for i, k in enumerate(kurtosis_values) if k threshold] if len(selected_idx) 0: selected_idx [np.argmax(kurtosis_values)] return np.sum(subbands[selected_idx], axis0) class MOMEDADeconvolution: def __init__(self, filter_length100, num_impulses5): self.L filter_length self.M num_impulses def compute_momeda(self, signal, period): N len(signal) target np.zeros(N) impulse_positions np.arange(0, N, int(period))[:self.M] target[impulse_positions] 1 X np.zeros((N - self.L 1, self.L)) for i in range(N - self.L 1): X[i] signal[i:i self.L] y target[self.L - 1:] XtX X.T X Xty X.T y f np.linalg.solve(XtX 0.01 * np.eye(self.L), Xty) return np.convolve(signal, f, modesame) class EnvelopeHarmonicProductSpectrum: def __init__(self, fs, max_harmonics5): self.fs fs self.max_harmonics max_harmonics def compute_ehps(self, signal): analytic hilbert(signal) envelope np.abs(analytic) N len(envelope) envelope_fft np.abs(fft(envelope))[:N//2] freqs fftfreq(N, 1/self.fs)[:N//2] hps np.ones_like(envelope_fft) for h in range(1, self.max_harmonics 1): decimated envelope_fft[::h] hps[:len(decimated)] * decimated return freqs, hps def estimate_fault_frequency(self, signal, freq_range(50, 500)): freqs, hps self.compute_ehps(signal) mask (freqs freq_range[0]) (freqs freq_range[1]) valid_freqs freqs[mask] valid_hps hps[mask] peaks, _ find_peaks(valid_hps, heightnp.mean(valid_hps)) if len(peaks) 0: return valid_freqs[peaks[np.argmax(valid_hps[peaks])]] return valid_freqs[np.argmax(valid_hps)] class SparseBayesianLearning: def __init__(self, max_iter500, tol1e-6): self.max_iter max_iter self.tol tol def build_dictionary(self, N, frequencies, fs): t np.arange(N) / fs D [] for f in frequencies: D.append(np.cos(2 * np.pi * f * t)) D.append(np.sin(2 * np.pi * f * t)) return np.array(D).T def fit(self, y, D): N, M D.shape alpha np.ones(M) beta 1.0 for _ in range(self.max_iter): Sigma np.linalg.inv(np.diag(alpha) beta * D.T D) mu beta * Sigma D.T y gamma 1 - alpha * np.diag(Sigma) alpha_new gamma / (mu**2 1e-10) beta_new (N - np.sum(gamma)) / np.sum((y - D mu)**2) if np.max(np.abs(alpha_new - alpha)) self.tol: break alpha alpha_new beta beta_new return mu, Sigma class KSVDDictionary: def __init__(self, n_atoms256, sparsity10, max_iter50): self.n_atoms n_atoms self.sparsity sparsity self.max_iter max_iter self.dictionary None def initialize_dictionary(self, signals): n_samples signals.shape[0] indices np.random.choice(n_samples, self.n_atoms, replaceFalse) self.dictionary signals[indices].T self.dictionary normalize(self.dictionary, axis0) def omp_sparse_code(self, signal): residual signal.copy() indices [] coefficients np.zeros(self.n_atoms) for _ in range(self.sparsity): correlations np.abs(self.dictionary.T residual) correlations[indices] 0 best_idx np.argmax(correlations) indices.append(best_idx) D_selected self.dictionary[:, indices] coef np.linalg.lstsq(D_selected, signal, rcondNone)[0] residual signal - D_selected coef coefficients[indices] coef return coefficients def fit(self, signals): self.initialize_dictionary(signals) for _ in range(self.max_iter): codes np.array([self.omp_sparse_code(s) for s in signals]) for j in range(self.n_atoms): indices np.where(codes[:, j] ! 0)[0] if len(indices) 0: continue E signals[indices].T - self.dictionary codes[indices].T np.outer(self.dictionary[:, j], codes[indices, j]) U, S, Vt np.linalg.svd(E, full_matricesFalse) self.dictionary[:, j] U[:, 0] codes[indices, j] S[0] * Vt[0] return self.dictionary class WeightedSparseClassifier(BaseEstimator, ClassifierMixin): def __init__(self, sparsity15): self.sparsity sparsity self.dictionary_learner KSVDDictionary() self.train_data None self.train_labels None def compute_sample_weights(self, test_sample): weights [] test_kurtosis np.mean((test_sample - np.mean(test_sample))**4) / (np.std(test_sample)**4 1e-10) test_skewness np.mean((test_sample - np.mean(test_sample))**3) / (np.std(test_sample)**3 1e-10) for train_sample in self.train_data: train_kurtosis np.mean((train_sample - np.mean(train_sample))**4) / (np.std(train_sample)**4 1e-10) train_skewness np.mean((train_sample - np.mean(train_sample))**3) / (np.std(train_sample)**3 1e-10) dist np.sqrt((test_kurtosis - train_kurtosis)**2 (test_skewness - train_skewness)**2) weights.append(np.exp(-dist)) return np.array(weights) def fit(self, X, y): self.train_data X self.train_labels y self.classes_ np.unique(y) self.dictionary_learner.fit(X) return self def predict(self, X): predictions [] for test_sample in X: weights self.compute_sample_weights(test_sample) residuals [] for c in self.classes_: class_mask self.train_labels c class_samples self.train_data[class_mask] class_weights weights[class_mask] weighted_reconstruction np.average(class_samples, axis0, weightsclass_weights) residual np.linalg.norm(test_sample - weighted_reconstruction) residuals.append(residual) predictions.append(self.classes_[np.argmin(residuals)]) return np.array(predictions)如有问题可以直接沟通