Remaining Useful Life Estimation of Rotating Machines using Octave Spectral Features

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

“…The vibration signals used for the methods was from the horizontal vibration sensors only, which are on the same plane as the applied load. Previous experiments have shown these to be superior for the prognostic purposes [3].…”

“…Study A involved a supervised ML classification incorporating the seven bearing test cases from the Pronostia Dataset under the first set of condition parameters [47]. ML classification models were trained and tested in a round robin approach [3,15]. It can be observed from the accelerometer recorded data presented in Figure 2 that three of the seven bearings tested exhibit a more sudden abrupt fault signature as opposed to the gradual, evolving degradation trend expected in a run-to-failure bearing trend.…”

“…Also, preventing abrupt failure modes from occurring significantly reduces health and safety concerns associated with electric and rotating machines. Catastrophic equipment failure for various mission-critical systems, such as aircraft and electrified vehicles can have serious health and safety consequences [1][2][3]. Accurate prediction of the remaining useful life (RUL) of components significantly reduces the potential for sudden failure modes to occur, allowing for timely repairs.…”

“…Data-driven bearing prognostic systems are constructed using signal processing techniques with real measured sensor acquired signals, to analyse and detect trends providing valuable evidence of system degradation [12,13]. Sensing modalities to acquire bearing degradation signatures that have been widely explored in recent years include vibration signals [1,3,14,15], acoustic emissions [16,17], stator current measurements [18][19][20], thermalimaging [21], and multiple sensor fusion [22,23]. Of these, vibration signals, acquired from mounted accelerometers is often attributed as the most favourable approach for conditionbased monitoring (CbM) in general, due to the non-invasive nature of the measurement data, low cost, robustness and ease of implementation in practice [24].…”

“…In recent years, a variety of Machine Learning (ML) algorithms have been proposed to perform the task of bearing RUL predictions. These algorithms vary in complexity and efficiency and often incorporate supervised ML approaches such as k-Nearest Neighbour (k-NN) and Support Vector Machines (SVM) [3,15,16], regression model approaches [14,39], deep learning models including convolutional neural networks (CNN) and deep belief networks (DBN) [41,42], blind deconvolution methods [43,44], and Bayesian probabilistic prediction models such as Kalman and particle filtering [45,46]. This paper introduces a low complexity prognostic monitoring approach for RUL estimation of mechanical rolling element bearings that builds further on previous work by the authors, which achieved RUL classification accuracy percentages of 74.3% using signal EA combined with novel feature engineering techniques [1].…”