Recent Patents on Engineering - Volume 14, Issue 3, 2020

The expansion of an average lifetime and increased cost of health analysis have resulted in effective methods for healthcare monitoring. A Wireless Body Area Network (WBAN) is used for continuous monitoring of patients to enhance health care and quality of life. As the sensors worn on the human body have a small size, low transmission power and restricted battery, which necessitate the development of energy-efficient routing protocols for increasing the network lifetime. This paper proposes an Optimized Energy Efficient and Quality-of-Service aware Routing Protocol (OEEQR) to achieve longer network lifetime, energy efficiency, lower delay and high throughput. In the proposed protocol, the cost function with residual energy, distance and path loss as its parameters is optimized using Particle Swarm Optimization (PSO) technique. The proposed cost function determines the best feasible next hop to send the data to the sink.

Background: With the growing popularity of various group communication applications, such as file transfer, multimedia events, distance learning, email distribution, multiparty video conferencing, and teleconferencing, multicasting seems to be a useful tool for efficient multipoint data distribution. An efficient communication technique depends on various parameters like processing speed, buffer storage, and amount of data flow between the nodes. If data exceeds beyond the capacity of a link or node, then it introduces congestion in the network. A series of multicast congestion control algorithms have been developed, but due to the heterogeneous network environment, these approaches do not respond nor reduce congestion quickly whenever network behavior changes. Objective: Multicasting is a robust and efficient one-to-many (1: M) group transmission (communication) technique to reduced communication cost, bandwidth consumption, processing time, and delays with similar reliability (dependability) as of regular unicast. This patent presents a novel and comprehensive congestion control method known as an integrated multicast congestion control approach (ICMA) to reduce packet loss. Methods: The proposed mechanism is based on a leave-join flow control mechanism along with a Proportional Integrated and Derivate (PID) controller to reduce packet loss, depending on the congestion status. In the proposed approach, the Proportional integrated and derivate controller computes expected incoming rate at each router and feedback this rate to upstream routers of the multicast network to stabilize their local buffer occupancy. Results: Simulation results using NS-2 exhibit the immense performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than other existing methods. Conclusion: The proposed congestion control scheme provides better bandwidth utilization and throughput than other existing approaches. Moreover, we have discussed existing congestion control schemes with their research gaps. In the future, we are planning to explore the fairness and quality of service issues in multicast communication.

Background: Privacy-enhancing techniques are developed in order to provide strong protection to cyberspace. These techniques aim to allow users to keep their identities hidden during the communication when they are sending an email, making payments online, browsing the Web, or posting to newsgroups. MixNet is the most practical solution for concealing identities of message and sender’s identities. Objective: It provides the sender and receiver anonymity as well as message security. The main goal of MixNet is to overcome vulnerability and enhance the performance of communication. It can efficiently handle the messages of various lengths and produce desirable results with privacy. The goal of this patent is to acquire information and concepts regarding MixNet. We also grant hints for future lookups and references. Methods: The design of MixNet depends on what the cryptosystem method is used. Symmetric and Asymmetric both are used. Other methods could be also used such as PIR, CSP and FDR model, RPC, Token-based approach, or others. Result: In this patent, we provided an overview of MixNet approaches and presented a survey on MixNet based techniques and different models. We also constructed the comparison tables as per our requirements for better understanding. For this purpose, we found a total of 120 articles related to the MixNet published between 1990 and 2018 from the most relevant scientific resources. However, only 86 papers were analyzed due to comprehensiveness and relevancy in this article. Conclusion: Focused on the security and privacy of communication and how it can be achieved. This patent also reveals research progress and research gap on MixNet.

The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, the Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

The efficient use of energy consumption in WSNETs is one of the most promising areas for researchers. In such networks, a considerable amount of bandwidth and energy is wasted for sensing the communication over the common channels. To recognise these complications, WSNETs offers dynamic allocation of the spectrum to optimise the bandwidth utilization. The S-MAC protocol which is based upon Time Division Multiple Access technique has already been proven as a better protocol for the efficient use of energy at MAC Layer. We revised all the patents related to the energy issues in Wireless Sensor Networks. The Hidden terminal problem is a well-known issue in wireless networks due to which the performance of the single-channel medium access protocols is affected. An adaptive listening technique at the MAC Layer was exploited under Dynamic Spectrum Access to address the issue. Adaptive listening proposes a dynamic sleep/idle period based on the data transmission pattern. Thus, the S-MAC protocol further improves the performance in terms of energy consumption and bandwidth utilization at the MAC Layer. The performance of this proposed model is compared with other protocols like IEEE 802.11 and SMAC with periodic sleep. The proposed model offers better network throughput and better energy consumption as seen in these simulation results under various parameters.

Background: Magnesium alloys and nanocomposites have been of great importance to automotive, aerospace and marine industries owing to their superior specific mechanical properties, impact resistance, superior damping capacities, and biocompatibility. Low-cost manufacturing of magnesium- based materials is the key to realize the high impact. We reviewed patents relating to production of magnesium- based materials using low cost techniques. Objective: Recent trends in the field of magnesium technology has driven researchers to develop magnesium materials applicable in both structural and biomedical applications. Incorporation of biocompatible secondary reinforcements into the magnesium matrix is important to meet the current requirements. Methods: In the current study, low cost naturally available eggshell particles are reinforced into magnesium- zinc alloy using powder metallurgy technique assisted microwave sintering technique and tested for a mechanical, thermal and damping response. Results: Addition of eggshell improved the grain size of the Mg2.5Zn alloy by ~60%. The microhardness values of Mg2.5Zn10ES composite is 73 Hv which is a significant 30% improvement when compared to Mg2.5Zn alloy (56 Hv). Enhanced thermal stability was observed with the presence of eggshell as Mg2.5Zn10ES composite did not self-ignite even at a temperature of 750°C. The compressive yield strength of the composite was ~25% greater than the alloy owing to superior grain refinement of ~60%. Conclusion: The presence of eggshell particles assisted in refining the microstructure, thereby significantly enhancing the compression properties of the Mg-2.5Zn alloy and led to a better thermal and dimensional stability of the synthesized composites. Structure-property correlations are drawn to understand the behavior of the composites.

Background: The motive of the Internet of Things (IoT) is to monitor and to control the devices that are connected to the internet. In IoT sensory environments, the application queries for the physical quantities in the spatiotemporal domain. The interaction between the sensors and the applications from the internet is the next big thing in the era of the internet of things. To minimise the resource utilisation, task scheduling mechanisms are implemented to the network. Methods: The PRITRAPS (Priority-based Task aware Pre-processing and Scheduling) is a mechanism that is employed in real time scenarios of industries. In which different applications units are accessing the gateway unit to measure and monitor the parameters of different service types. PRITRAPS employs priority among the tasks to reduce the network load. Results: The QoS parameters of the system are analysed and compared with the previous methodologies. The PRITRAPS mechanism consists of a task pre-processor unit, Scheduler and EMS module within the gateway unit. The scheduling algorithm employed in PRITRAPS is EDF (Earliest Deadline First) algorithm. The pre-processing task unit decreases the number of tasks by choosing the tasks having similar spatial and temporal requirements. The residual energy of the sensor nodes can help the scheduler for deciding the sensor nodes in respective of task requirements. The scheduler finds the best potential nodes and assigns them to the task for processing. Conclusion: To reduce the tasks arrivals at the wireless sensor unit, a priority based CCTs (Critical Covering Task sets) is proposed, and it effectively reduces the packet congestion and network overload. The results obtained are satisfactory and proven that PRITRAPS outperform TRAPS in energy consumption of a node by processing the tasks on the node. PRITRAPS require only 50 % of the time that has been taken by TRAPS for serving the tasks. The PRITRAPS mechanism is implemented in NS3 simulator and tested for different task sets.

Background: Duplex stainless steel (DSS) has gained increasing interest in recent years for a number of applications as structural materials in various industrial sectors of the petrochemical process plant, marine engineering and automotive industries. However, this material has experienced hardness and wear failure in the service. Therefore, new development in the surface modification for DSS is required to explore the possibility of producing a hard modified surface layer of SiC resolidified layer by TIG torch surface melting technique. Methods: TIG torch surface melting technique was performed on DSS substrate with preplaced SiC reinforcement. The effects of particle size, SiC preplacement, heat input and shielding gas flow rate on surface topography, hardness and wear rate were investigated through several characterizationsand tests. Results: Inspection of the surface topography reveals rippling marks which proved that the resolidification process occurred during the TIG torch surface melting technique. The obtained result showed that the preplacement of SiC reinforcement on DSS via TIG torch surface melting technique could increase the hardness of DSS by ~ five times. From Taguchi analysis, the optimum combination of parameters obtained for the lowest wear rate of surface layered DSS was: preplacement rate, 1.5 mg/mm², SiC particles size, 60 μm; heat input, 720 J/mm; and gas flow rate, 15 L/min. Conclusion: The results of this study confirmed that conventional TIG torch melting technology may be used as an alternative to the more expensive laser or plasma technique to create a new composite surface layer on DSS material.

Background: Organic Light Emitting Diodes have emerged as a potential candidate for being used as the display because of their remarkable performance. In the past decade, a lot of interest was shown in their development and improvement. Scientists utilized different methods for enhancing their performance which included the change in OLEDs architecture. Different supporting layers (charge injection, transportation and blocking) are added in the architecture to enhance the performance of OLED. Some of the patents related to the application of the OLED have also been reviewed to observe the utilization of the present work in those applications. Objective: The motive of this research article is to analyze impacts the impact of different layers on the performance of OLED. Methods: The objective is achieved by systematically analyzing the impact of different layers on the OLED. The analysis commenced by investigating the properties of the basic OLED consisting of the emission layer only. Thereafter, in succession different supporting layers are added to the OLED architecture. Subsequently, after adding each layer, OLED device is analyzed and its properties are determined. Finally, the performance of different layers is compared. Results: The outcome of the above research study shows that these supporting layers are significantly able to enhance the performance of the OLED. Step by step layers are added to the architecture and enhancement in the performance is observed. There is an improvement of 13% to 38% not over the basic OLED architecture, but over the multilayered OLED device, which is already far better than the basic OLED. Conclusion: Therefore, from the analyses, it can be concluded that the different layers are significant for the OLED architecture and improve the performance of the OLED considerably.

Background: The undesirable effect of power quality issues on distribution system due to power electronics based controllers; it is highly desired to design a novel power quality conditioner with a minimum number of switches. The proposed configuration reduces the switching loss and also effectively alleviates the power quality issues. This paper introduces the configuration of 3P4SW (Three Phase Four Switch) UPQC (Unified Power Quality Conditioner) for both shunt and series APF (Active Power Filter) as well as 3P9SW (Three Phase Nine Switch) UPQC topologies, which can be implemented in medium voltage power grid. The major challenge of phase balance with only four switches in UPQC is accomplished by adaptive Self-tuning PID using neuro-fuzzy logic control and adaptive reference current generation scheme. This controller improves sag and swells compensation with better angle control via shunt and series converter performance and passive components design. The efficacy of proposed topology is tested on MATLAB/Simulink software and results are compared with the existing three phase six switch configuration (3P6SW). Methods: The main objective of this work is to identify the most suitable configuration of UPQC to mitigate the power quality issues in power distribution system. to achieve this goal a Synchronous reference frame theory is used to generate the pulses in the back to back connected VSI in UPQC. Results: The suggested topologies should not compromise with the nominal functions of the controller; hence from the simulation analysis it clear that the 3P4SW UPQC, as well as 3P9SW UPQC topologies, will fulfill the given constraints and also it maintains the magnitude of the load voltage as per the standard values. Also, the THD percentage is maintained well within the IEEE standard. Conclusion: The proposed topologies can be implemented for medium voltage distributed system with improved power quality.

Background: The large amount of data emanated from social media platforms need scalable topic modeling in order to get current trends and themes of events discussed on such platforms. Topic modeling play crucial role in many natural language processing applications like sentiment analysis, recommendation systems, event tracking, summarization, etc. Objectives: The aim of the proposed work is to adaptively extract the dynamically evolving topics over streaming data, and infer the current trends and get the notion of trend of topics over time. Because of various world level events, many uncorrelated streaming channels tend to start discussion on similar topics. We aim to find the effect of uncorrelated streaming channels on topic modeling when they tend to start discussion on similar topics. Methods: An adaptive framework for dynamic and temporal topic modeling using deep learning has been put forth in this paper. The framework approximates online latent semantic indexing constrained by regularization on streaming data using adaptive learning method. The framework is designed using deep layers of feedforward neural network. Results: This framework supports dynamic and temporal topic modeling. The proposed approach is scalable to large collection of data. We have performed exploratory data analysis and correspondence analysis on real world Twitter dataset. Results state that our approach works well to extract topic topics associated with a given hashtag. Given the query, the approach is able to extract both implicit and explicit topics associated with the terms mentioned in the query. Conclusion: The proposed approach is a suitable solution for performing topic modeling over Big Data. We are approximating the Latent Semantic Indexing model with regularization using deep learning with differentiable 132;“1 regularization, which makes the model work on streaming data adaptively at real-time. The model also supports the extraction of aspects from sentences based on interrelation of topics and thus, supports aspect modeling in aspect-based sentiment analysis.

Background: Due to the increasing growth of social websites, a lot of user-generated data is available these days in the form of customer reviews, opinions, and comments. Objective: Sentiment analysis includes analyzing the user reviews and finding the overall opinions from the reviews in terms of positive, negative and neutral categories. Sentiment analysis techniques can be used to assign a piece of text a single value that represents opinion expressed in that text. Sentiment analysis using lexicon approaches is already studied. Methods: A new approach to sentiment analysis using deep neural networks techniques is proposed. Deep neural networks using Sequence to sequence model is studied in this paper. The main objective of this paper is to identify the sequence of relationships among the words in the reviews. Customer reviews are taken from Amazon and sentiment analysis is done using the word embedding method. Results: The results obtained by the proposed method are compared with the baseline algorithms such as Naïve, and logistic regression. Conclusion: Confusion Matrix along with receiver operating characteristics and area under the curve is analyzed. The accuracy of the proposed methodology is compared with other algorithms.

Background: In recent time with the growth of the technology and the business model, customer attrition analysis is considered as a very important metric which decides the revenues and profitability of the organization. It is applicable for all the business domains irrespective of the size of the business even including the start-ups. Because about 65% revenue for the organization comes from the existing customer. The goal of the customer attrition analysis is to predict the customer who is likely to exit or churn from the current business organization. In this research work, the literature review is carried out to explore the related work which has been already carried out in the field of customer attrition analysis. The literature review also focuses on some of the patents which are issued in the area of customer attrition or churn analysis. The goal of the research paper is to predict accurately the customer attrition rate in the Banking Sector. Objective: The main objective of this paper is to predict accurately the attrition rate in the Banking sector using an optimized deep feed-forward neural network. Methods: In the proposed work the predictive machine learning model is implemented using the optimized deep feed-forward neural network having five hidden layers in it. The model is trained using Adam optimizer algorithm to obtain the optimal accuracy. The Banking Churn data set is passed as input to the Optimized Deep Feed Forward Neural Network Model. In order to perform the comparative analysis, the same data set is passed as input to the other machine learning algorithm such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, and Artificial Neural Network. Results: The test results indicate that the proposed optimized deep feedforward neural Network model performed better in accuracy compared to existing machine learning techniques. Conclusion: The proposed optimized deep neural network model is an accurate model for customer attrition analysis in the Banking sector compared to the existing machine learning techniques.

Background: To predict the future health situation of a nation or a state, the Time Series Predictive Modelling is a valuable tool in the health system. In order to attain the Millennium Development Goals (MDGs) for MMR in India, the purpose of this research work is to identify the trends in the essential components, which are responsible for maternal death prevention. Methods: To achieve the above-mentioned objective we have evaluated the performance of three different approaches in our process model for Time Series Predictive Modelling on public maternal health data. The first approach is Exponential Smoothing method i.e. is a statistics based method, the second one is Multi Layer Perceptron method i.e. a machine learning based method and the third one is Long Short Term Memory network i.e. a deep learning method. For the data analysis, the five years’ monthly time series data (2012 to 2017) of Uttar Pradesh state of India is collected from NRHM portal of Indian Government. It is partitioned into training data for modeling and testing data for validation of the model. Results: The major components from the original data are selected by using an attribute selection method i.e. greedy approach based Best-First Wrapper method. The MAPE statistical parameter is used to define the accuracy level of the predictive values for the selected dimensions of the given data. A performance-based comparison of all applied approaches is presented at last which illustrates that exponential smoothing method has performed better than the other two methods. Conclusion: The presented trend analysis and future values generated by using the presented process model will feed input in the decision making for planning better healthcare services.

Social media, in general, and Twitter, in particular, provide a space for discourses, contemporary narratives besides a discussion about few specific social issues. People respond to these events by writing short text messages. Background: Hashtag “#” , a specific way to respond to a given raised discourse, narrative or any contemporary issue is usual to social media. Netizens write a short message as their opinion about any given issue represented using a given Hashtag. These small messages generally tend to have a latent topic (theme) as one’s opinion about it. Objective: This research is aimed to extract, represent and understand those hidden themes. Method: Biterm Topic Model (BTM) has been used in this study given its ability to deal with the short messages unlike Latent Dirichlet Allocation that expects a document to have a significant length. Results: Twitter Hashtag #M comments. Data has been modelled with ten (10) topic. Conclusion: The experimental results show that the proposed approach to understand the twittter hashtages from latent themes using biterm topic modelling method is very effective as compared to other methods.

Background: In this astronomically immense world tremendous amount of data engendering in every minute from the different domain which is referred to Big Data. In the last few years the data is incrementing day by day across the world. This Research fixates on the analysis of malefaction rates of 5 different states year wise, all the analysis is done utilizing Apache Pig. Methods: The goal of the work is to analyze the astronomically immense malefaction data and find the estimate number of malefaction transpires in sundry states. This is done in Apache pig environment utilizing “Pig Latin” as language. A short code is indicted in Pig Latin which is utilized to load and process the data into Map reduce environment, afterwards the result are obtained with the detail of minimum and maximum mapper and reducer timing. Result: The data is visualized into graphs to make analysis to analyze the variation of malefaction rates in distinct states. After analyzing the malefaction against women, murder cases are very high in 2006-2010 as compared to other year groups whereas abducting and rape cases incremented perpetually from 2001 to 2014 respectively. Similarly all the reports regarding to different malefaction rates are visualized above by utilizing graphs. Conclusion: Various results are found with sundry queries and everything is represented graphically for better understanding and comparison. This avails us to find which state is affected by which crime. The expeditiousness of Apache pig can additionally be optically discerned as this immensely colossal crime data processed in short time with precision.

Background: Image forensics deal with the problem of authentication of pictures or their origins. There are two types of forensics techniques namely active and passive. Passive forgery is also known as blind forensics technique. In passive forgery, copy-move (cloning) image forensics is most common forgery technique. In this approach, an object or region of a picture is copied and positioned somewhere else in the same image. The active method used watermarking to solve picture genuineness problem. It has limitations like human involvement or particularly equipped cameras. To overwhelm these limitations, numerous passive authentication approaches have been developed. Moreover, both approaches do not require any prior information about the picture. Objective: The prime objective of this survey is to provide an inclusive summary as well as recent advancement, challenges and future direction in image forensics. In today’s digital era, digital pictures and videos are having a great impact on our life as well as society, as they became an important source of information. Though earlier it was very difficult to doctor the picture, nowadays digital pictures can be doctored easily with the help of editing tools and the internet. These practices make pictures as well as videos genuineness deceptive. Conclusion: This paper presents the current state-of-the-art of passive (cloning) image forensics techniques, challenges and future direction of this research domain. Furthermore, the major open issues in developing a robust cloning image forensics detector with their performance are discussed. Lastly, the available benchmark datasets are also discussed.

Multiple-Input Multiple-Output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model that considers all the dominating effects of the wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for Single-Input Single-Output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for the MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of the MIMO system is also required in such a scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the nearoptimal system performance in a real-world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection, and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

In this paper, medical images are used to realize the Computer-Aided Diagnosis (CAD) system, which develops targeted solutions to existing problems. Relying on the MiCOM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows the realization of diagnosis and treatment and sharing of data resources.

Large amount of data coming from different sources and formats is available on the web which generates heterogeneity problem. Semantic web technologies play an important role for collecting, merging, matching and aggregating big data from heterogeneous resources by determining the semantic correspondence between the entities. However, achieving good efficiency is major challenge for large scale ontology matching task. Objective: We propose a PBOM framework for coping with the large scale ontology matching problem. Methods: Our proposal first selects the source ontology and calculates the similarity of concepts within source ontology by using Lin measure. We use clustering algorithm for partition of the source ontology, obtained clusters of source ontology then used to divide the target ontology. During matching process, we run matchers from the pool of the matchers over each clusters. We aggregate the result of element level matchers and structure level matchers according to weighted sum aggregation. Each cluster is executed in its processor in parallel with other clusters. Result: We have presented step wise execution of proposed approach over one cluster of source and target ontology. The evaluation of our framework is performed by OAEI datasets of bibliographic benchmark 2014, biomedical track 2015 and anatomy 2016. Conclusion: Results show that, the performance of our approach is better in term of F-measure. The combination of clustering algorithm and parallel processing reduces the memory space and time complexity of matching process.

Background: In order to better realize the real-time on-line monitoring of the working state of the coal mill and determine the status of the equipment used in the coal mill, the intelligent embedded system of the coal mill based on DSP was studied. Methods: Firstly, the working requirement of the coal mill and the task of data acquisition system have been briefly introduced, and the requirement of the overall embedded system design has been put forward. Then, the structure design was analysed including both hardware and software, and the embedded system design including digital signal processing, and data acquisition was completed. All patents related to intelligent embedded data acquisition and storage system are described in detail. Results: After that, the function test of the actual coal mill embedded system was carried out, which proved the practicability and reliability of the designed embedded system for coal mill and based on DSP. Conclusion: The embedded system improves the automation level of coal mill, and is of great significance to the on-line monitoring and research of large-scale machinery and equipment in the future.

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine (SVM) models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.