Multidimensional prognostics for rotating machinery: A review

open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment

Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones

AbstractDynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life (RUL) for the deteriorating systems. This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically monitored degradation processes with dynamic time-varying operational conditions and condition-specific failure zones. The method assumes that the degradation rate is influenced by specific operational condition and moreover, the transition between different operational conditions plays the most important role in affecting the degradation process. These operational conditions are assumed to evolve as a discrete-time Markov chain (DTMC). The failure thresholds are also determined by specific operational conditions and described as different failure zones. The 2008 PHM Conference Challenge Data is utilized to illustrate our method, which contains mass sensory signals related to the degradation process of a commercial turbofan engine. The RUL estimation method using the sensor measurements of a single sensor was first developed, and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy. The effectiveness and advantages of the proposed method are presented in a comparison with existing methods for the same dataset

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminium electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature

DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE

Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems. The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different. A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics. For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance. A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior. A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation. A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form. Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data

Landscape-level responses of boreal forest bird communities to anthropogenic and natural disturbance

In an attempt to manage values other than timber production, forestry companies have sought a new paradigm to manage forest resources. Based on the hypothesis that wildlife in the boreal forest has adapted to habitat structures created by natural disturbances, some forest harvests have been modified to approximate patterns left after natural disturbance. Attempts at approximating natural disturbance have included retention of patches of live trees within cutblock boundaries, cutting to natural stand boundaries and application of harvest plans with spatio-temporally aggregated cutblocks (single-pass harvests). Single-pass harvesting is a recent attempt to better approximate natural disturbance in the boreal and has not been evaluated for its potential to sustain wildlife. I therefore contrasted residual patch pattern and composition, as well as landscape-scale avian abundance and composition in 1) single-pass; 2) multi-pass; and 3) salvage logged post-fire harvests, and contrasted these with unsalvaged post-fire sites. Post-fire sites were used to define the Natural Range of Variation (NRV). Seventy-two plots (12 post-fire, 15 post-salvage harvest, 16 single-pass harvest, and 29 multi-pass harvest) were surveyed for avian community composition and abundance one to five years post disturbance. I contrasted the composition of remaining live forest stands at the landscape-scale and in residual patches by pairwise comparison of pre- and post-disturbance composition. At the landscape-scale, non-metric mutlidimensional scaling suggested post-disturbance landscape composition of post-fire and salvage-logged plots was similar to pre-disturbance landscape composition, with a tendency toward greater survival of hardwoods and lower survival of jack pine (Pinus banksiana) or black spruce (Picea mariana). However, harvesting of hardwoods and mixedwood stand types in single and multi-pass harvests led to landscapes with more bog and swamp habitats. Comparison of residual patch composition with pre-disturbance composition was made using blocked multi-response permutation procedures. Post-fire plots (i.e. NRV) had residual patches that were representative of pre-disturbance composition, but with slightly more hardwoods and less black spruce/jack pine than expected by chance. All harvested treatments had similar biases among residuals to those left by fire, except that multi-pass harvests tended to leave less mixedwood habitat than expected. Multi-pass harvests also had less area in residual patches, and patches were smaller, more isolated and less complex in shape. Single-pass harvests had residual patches that were more representative of the size, shape, complexity, and change in composition seen post-fire. Multi-pass harvests only had 14% of the residual patch area in patches at least 5 ha in size, whereas this proportion was higher in fire (83%), salvage-logged areas (42%), and single-pass harvests (57%). Old-growth associated species might only persist in patches 5 ha or larger, and so multi-pass harvesting may have negative consequences for these forest birds.Redundancy analysis indicated that bird communities differed from the NRV in all harvest treatments. However, single-pass harvests provided a slightly better fit to NRV than did multi-pass harvesting. Community similarity was influenced by non-linear responses to area harvested, residual retention, residual composition and pre-disturbance forest composition. An optimization routine was used to select harvest characteristics that would maximize community similarity to NRV. Optimization suggested that community similarity to NRV can be maximized by using single-pass harvests over multi-pass harvests, harvesting 66-88% of of a planning unit, and retaining 5-19% of the harvest area as live residual patches.My results suggest that single-pass harvesting may be ecologically more sustainable than multi-pass harvests. Future studies are required to determine whether both harvesting treatments converge with NRV through time. Greater overlap of salvage-logged avian communities with NRV suggests that experimentation with prescribed fire as a post-harvest treatment may be the best method to bring harvests ecologically closer to NRV, and highlights the need to conserve early post-fire habitats