Maoyin Chen

A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution

Remaining useful life (RUL) estimation is regarded as one of the most central components in prognostics and health management (PHM). Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults. In this paper we consider the problem of estimating the RUL from observed degradation data for a general system. A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian updating and expectation maximization (EM) algorithm. The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this paper, which makes the estimated RUL depend on the observed degradation data history. As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of our presented approach. A major contribution under these two special cases is that our approach can obtain an exact and closed-form RUL distribution respectively, and the moment of the obtained RUL distribution from our presented approach exists. This contrasts sharply with the approximated results obtained in the literature for the same cases. To our knowledge, the RUL estimation approach presented in this paper for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history. Finally, numerical examples for RUL estimation and a practical case study for condition-based replacement decision making with comparison to a previously reported approach are provided to substantiate the superiority of the proposed model.

Some Simple Synchronization Criteria for Complex Dynamical Networks

Based on the concept of matrix measure, some simple synchronization criteria for complex dynamical networks are provided. If the coupling strength and the largest nonzero eigenvalue of the coupling matrix satisfy certain conditions, the stability of the synchronization manifold can be ensured. Furthermore, the proposed criteria are less conservative than some existing criteria

Output Tracking Control for Networked Systems: A Model-Based Prediction Approach

This paper studies the problem of output tracking for networked control systems with network-induced delay, packet disorder, and packet dropout. The round-trip time (RTT) delay is redefined to describe these communication constraints in a unified way. By including the output tracking error as an additional state, the output tracking problem is converted into the stabilization problem of an augmented system. Based on the observer of the original state increment and the feedback of the output tracking error, a model-based networked predictive output tracking control (NPOTC) scheme is proposed to actively compensate for the random RTT delay. The closed-loop stability is proved to be independent of the RTT delay, and the separation principle for the design of the observer-based state feedback controller is still held in the NPOTC system. A two-stage controller design procedure is presented, which not only guarantees the stability of the closed-loop NPOTC system but also achieves the same output tracking performance as that of the local control system for time-varying reference signals. Both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

Synchronization in uncertain complex networks

We consider the problem of synchronization in uncertain generic complex networks. For generic complex networks with unknown dynamics of nodes and unknown coupling functions including uniform and nonuniform inner couplings, some simple linear feedback controllers with updated strengths are designed using the well-known LaSalle invariance principle. The state of an uncertain generic complex network can synchronize an arbitrary assigned state of an isolated node of the network. The famous Lorenz system is stimulated as the nodes of the complex networks with different topologies. We found that the star coupled and scale-free networks with nonuniform inner couplings can be in the state of synchronization if only a fraction of nodes are controlled.

A model for real-time failure prognosis based on hidden Markov model and belief rule base

As one of most important aspects of condition-based maintenance (CBM), failure prognosis has attracted an increasing attention with the growing demand for higher operational efficiency and safety in industrial systems. Currently there are no effective methods which can predict a hidden failure of a system real-time when there exist influences from the changes of environmental factors and there is no such an accurate mathematical model for the system prognosis due to its intrinsic complexity and operating in potentially uncertain environment. Therefore, this paper focuses on developing a new hidden Markov model (HMM) based method which can deal with the problem. Although an accurate model between environmental factors and a failure process is difficult to obtain, some expert knowledge can be collected and represented by a belief rule base (BRB) which is an expert system in fact. As such, combining the HMM with the BRB, a new prognosis model is proposed to predict the hidden failure real-time even when there are influences from the changes of environmental factors. In the proposed model, the HMM is used to capture the relationships between the hidden failure and monitored observations of a system. The BRB is used to model the relationships between the environmental factors and the transition probabilities among the hidden states of the system including the hidden failure, which is the main contribution of this paper. Moreover, a recursive algorithm for online updating the prognosis model is developed. An experimental case study is examined to demonstrate the implementation and potential applications of the proposed real-time failure prognosis method.

Synchronization in Complex Dynamical Networks With Random Sensor Delay

This brief proposes a new complex dynamical network model, in which nodes are connected by measured outputs experiencing the random sensor delay. This model is totally different from some existing network models. Then, synchronization in the proposed network model is analyzed by the stochastic stability theory. A sufficient synchronization condition is given to ensure that the proposed network model is exponentially mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.

A sequential learning algorithm for online constructing belief-rule-based systems

A belief rule base inference methodology using the evidential reasoning (RIMER) approach has been developed recently. A belief rule base (BRB), which can be treated as a more generalized expert system, extends traditional IF-THEN rules, but requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees. These parameters need to be determined with care for reliable system simulation and prediction. Some off-line optimization models have been proposed, but it is expensive to train and re-train these models in particular for large-scale systems. Moreover, the recursive algorithms are also proposed to fine tune a BRB online, which require less calculation time and satisfy the real-time requirement. However, the earlier mentioned learning algorithms are all based on a predetermined structure of the BRB. For a complex system, prior knowledge may not be perfect, which leads to the construction of an incomplete or even inappropriate initial BRB structure. Also, too many rules in an initial BRB may lead to over fitting, whilst too few rules may result in under fitting. Consequently, such a BRB system may not be capable of achieving overall optimal performance. In this paper, we consider one realistic and important case where both a preliminary BRB structure and system parameters assigned to given rules can be adjusted online. Based on the definition of a new statistical utility for a belief rule as investigated in this paper, a sequential learning algorithm for online constructing more compact BRB systems is proposed. Compared with the other learning algorithms, a belief rule can be automatically added into the BRB or pruned from the BRB, and our algorithm can also satisfy the real-time requirement. In addition, our algorithm inherits the feature of RIMER, i.e., only partial input and output information is required, which could be either incomplete or vague, either numerical or judgmental, or mixed. In order to verify the effectiveness of the proposed algorithm, a practical case study about oil pipeline leak detection is studied and examined to demonstrate how the algorithm can be implemented.

Synchronization in small-world networks.

In this paper we consider complete synchronization in small-world networks of identical Rössler oscillators. By applying a simple but effective dynamical optimization coupling scheme, we realize complete synchronization in networks with undelayed or delayed couplings, as well as ensuring that all oscillators have uniform intensities during the transition to synchronization. Further, we obtain the coupling matrix with much better synchronizability in a certain range of the probability p for adding long-range connections. Direct numerical simulations fully verify the efficiency of our mechanism.

Multi-Sensor Information Based Remaining Useful Life Prediction With Anticipated Performance

For a class of multi-sensor dynamic systems subject to latent degradation, the remaining useful life prediction with anticipated performance is mainly considered in this paper. The hidden degradation process is first identified recursively by adopting distributed fusion filtering based on observations from multiple sensors. Then the remaining useful life distribution is predicted on the basis of converged degradation state and parameter updating during the operating process. The uncertainty index is aanalyzed to quantitatively evaluate the benefits of increasing multi-sensor information for predicted remaining useful life, and the sensor selection is also discussed for satisfying the anticipated performance such as variance. Our main results are verified by a numerical example, and a practical case study of the milling machine experiment.

Cooperative Predictive Maintenance of Repairable Systems With Dependent Failure Modes and Resource Constraint

Many works on condition-based maintenance of repairable systems apply to either a single failure mode, or statistically independent failure modes. Different from these works, this paper considers the problem of predictive maintenance of repairable systems with dependent failure modes, and resource constraints. Assume that (i) a repairable system is subject to two statistically dependent failure modes bidirectionally affecting each other, (ii) imperfect maintenance actions are cooperatively performed on two dependent failure modes by allocating insufficient resources spent for maintenance, and (iii) future maintenance scheduled at the current time depend on both the predicted number of future failures and the minimization of the expected maintenance cost rate defined in the long term. To resolve the above problem, a novel cooperative predictive maintenance model is proposed. Its basis is the incorporation of the hazard-rate function, and effective age. In this model, two failure modes are statistically dependent in such a way that the hazard rate of one failure mode depends on the accumulated number of failures of the other failure mode. The effect of imperfect maintenance is interpreted in terms of how the hazard rate function and the effective age are changed by maintenance actions. The age reduction factor for each failure mode due to maintenance has some deterministic relation to the degree of resources cooperatively allocated to perform maintenance. The decision variables in the maintenance policy, namely the number of maintenance actions to be performed, the interval between successive maintenance actions, and the cooperatively allocated degree of resources, can be recursively updated when new monitored information arrives. This approach relies on both the predicted number of future failures, and the minimization of the expected maintenance cost rate defined in the long term.