Exemplo n.º 1
0
	def pos_monotonicity(self, data):
		'''
		Positive Prognosability: Characterizes an increasing trend. 
		Liao, Linxia, Wenjing Jin, and Radu Pavel. 
		"Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment." 
		IEEE Transactions on Industrial Electronics 63.11 (2016): 7076-7083.
		'''
		utils = Utils()
		temp1 = np.diff(data, axis=0)
		temp2 = np.diff(data, 2, axis=0)
		score = (utils.count_pos(temp1)/(data.shape[0]-1) + utils.count_pos(temp2)/(data.shape[0]-2))/2
		return score
Exemplo n.º 2
0
	def monotonicity(self, data):
		'''
		Monotonicity characterizes an increasing or decreasing trend. 
		It can be measured by the absolute difference of “positive” and “negative” derivatives for each feature.

		    Javed, Kamran, et al. "Enabling health monitoring approach based on vibration data for accurate prognostics." 
		    IEEE Transactions on Industrial Electronics 62.1 (2014): 647-656.

		Monotonicity will only the metrics with time
		'''
		utils = Utils()
		temp = np.diff(data, axis=0)
		score = np.abs(utils.count_pos(temp)-utils.count_neg(temp))/(data.shape[0]-1)
		return score