fault detection

Using signal processing methods for fault detection of machinery is increasing nowadays. Noises added to the signal can negatively effect on efficiency of these methods. Time-domain averaging is a usual method to increase the strength of a signal. But, success of averaging is depends on an assumption which is corresponding points in averaging has same angle on the axle which is called synchronization. A little difference in synchronization can cause more decrease in efficiency of the method. Using tachometer is a usual way for synchronization. But, precision of tachometer isn’t enough. In this paper, as an attempt to solve this problem, a new method based on correlation is proposed for averaging which can synchronise data more precisely. Proposed method has been tested on real data gathered from a five-speed car gearbox. Using real-world data including a lot of components is an advantage of this research. Experimental results shows proposed method can reduce mean squared error from 0.419 to 0.103 in average which is a significant reduce.

Inasmuch as sensor nodes are typically used in inaccessible environments, they are vulnerable and insecure against environmental destructive factors and against deliberate devastating attempts of enemies. Hence, fault occurrence in wireless sensor networks (WSNs) is deemed to be an unavoidable phenomenon. The main drawback of comparative fault detection methods are that in case more than half of the neighboring nodes are faulty or the nodes become faulty due to a common mode failure (CMF), they will fail to detect faulty nodes properly. Thus, in order to address this issue, the authors introduced a cluster-based hierarchical fault detection method which increases the influence of non adjacent sensor nodes’ data in determining of sensor’s status. Therefore the proposed method not only compares the data of neighboring nodes but also compares the data of non-neighboring nodes at an upper layer in order to adopt the proper decision upon the status of the nodes. Since applying fault detection methods in determined intervals and static manner are considered as inefficient, in this paper, we put forward an intelligent and dynamic method to determine the appropriate time for the implementation of the fault detection algorithm; hence, the right time and the required number of the implementation of the algorithm are intelligently and dynamically specified and as a result, the network lifetime increases. The related simulations were carried out by means of Matlab software was conducted under different densities of the nodes and with differing probability of being faulty nodes. The simulations results indicated that the fault detection accuracy of the proposed algorithm is significantly high and its false alarm rate is noticeably low. The results obviously demonstrate that the proposed method is scalable.