Muhammad Aman Sheikh

Self-schedule and self-distributive MAC scheduling algorithms for next-generation sensor networks

The distributive nature of wireless sensor networks (WSNs) poses great challenges for the design of distributive scheduling to maximize network life and spatial reuse of time slot with minimum frame length. Most of the existing scheduling techniques are either centralized or contentional. The existing techniques cannot efficiently adapt to the dynamic wireless environment. In this paper, self-scheduled and distributed MAC (SSD-MAC) and self-distributive MAC (SD-MAC) medium access control algorithms are proposed to reduce the complexity and variety of scheduling problems. The proposed algorithms do not require any synchronization and can effectively adapt to dynamic topology changes without incurring global communication overhead. According to the proposed algorithms, each node maps a conflict-free time slot for itself up to 2-hop neighboring nodes. Consequently, each node successfully schedules a unique time slot for itself in a heuristic manner based on its local information. Moreover, the proposed algorithms also guarantee conflict-free edge coloring because all the incident edges to a single node are assigned to colors in such a way that none of the edges should have the same color. It has been demonstrated that, with regard to communication overhead, energy consumption and execution time through simulation proposed that algorithms outperform existing distributed randomized scheduling algorithm (DRAND).

An improved distributed scheduling algorithm for wireless sensor networks

The emergence of low power consumption, high data rate and small size sensor network applications, has increased the demand for high-performance network services. To meet this challenge, we propose an Improved Distributed Scheduling Algorithm (IDSA), a novel heuristic scheduling technique that can provide effective collision free broadcasting, lower energy consumption, minimum message overhead and enhanced channel utilization. In contrast to earlier traditional scheduling algorithms of medium access control (MAC), which are generally designed for sequential slot assignments, this paper presents an improved algorithm for distributed scheduling. The IDSA has several unique features. First, it optimizes energy through collision free transmission by scheduling conflict-free slots. Second, it can adapt the changes in topology explicitly without reconstructing the global transmission schedule with minimum message overhead. Furthermore, the IDSA also provides improved performance in terms of message overhead, slot assignment per round and energy consumption. Simulation results show that the IDSA significantly outperforms a representative distributed random slot assignment algorithm (DRAND).

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The noninvasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented.

Unsupervised on-line method to diagnose unbalanced voltage in three-phase induction motor

In this paper, an unsupervised automatic method based on a current signature neural network (NN) is presented to on-line diagnose stator fault without the inspection of any supervisor or technician. To extract the fault regime, the knowledge of current signature will not be enough; therefore, mathematical model, numerical analysis, as well as artificial intelligence (AI) are taken into account to extract the exact unbalanced voltage stator fault. Analytical expressions are derived for a stator conductor segment in order to find out the conductors that are responsible for the generation of magnetomotive force (MMF). A test rig is designed using three-phase induction motor, two-axis PASPORT sensor, PC, and PASCO interface to compute the effect of MMF at the stator side through a new series of harmonics which are helpful to tackle the scrupulous effect of an unbalanced voltage at the incipient stage. Further, an unsupervised NN has been introduced that endeavors the principal components of the new series of harmonics. The statistical parameters of a new series of harmonics are contemplated as input features for NN that not only diagnose unbalanced voltage but also identify the degree of unbalanced voltage through feed-forward multilayer perceptron (MLP) trained by backpropagation. The validation and performance of proposed methods have been theoretically and experimentally analyzed on a custom-designed test rig under various levels of unbalanced voltage. Moreover, the NN classification method shows higher accuracy with enough robustness to various levels of unbalanced voltage, which states that the proposed method is suitable for the real-world applications.

A Hardware and Software Integration Approach for Development of a Non-invasive Condition Monitoring Systems for Motor-Coupled Gears Faults Diagnosis

A non-invasive condition monitoring system for diagnosis of faults is vital for induction motors to operate safely and reliably. The currently used invasive techniques need direct access to the motor to collect and analyze data. Furthermore, the sensors used in invasive techniques are relatively expensive. This paper presents the development of hardware and software integrations for non-invasive diagnostic system to monitor specifically motor-coupled gear defects. The proposed system employs instantaneous power analysis, a unique technique for diagnostic condition monitoring which allows real-time non-stop tracking as well as assesses the severity of the defects. This technique can be adopted for decision-making that is not only fast but reliable. The severity of different gear defects have been studied experimentally, and the results were analyzed. The effectiveness of the proposed method has been verified through experimentation from the actual hardware implementation through the system-design platform and development environment software tool, LabVIEW.

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

This chapter provides a comprehensive analysis of noninvasive methods to diagnose stator winding insulation faults of an induction motor. Further, a novel noninvasive method is proposed to diagnose the root cause of winding failure due to unbalanced voltage to avoid catastrophic failure. Therefore, a winding function approach is utilized to derive an analytical expression for stator winding distribution andmagnetomotive force (MMF). This tactic qualifies the conductor segment that generates MMF, and it also helps to analyze a healthy current spectrum. One can easily observe higher order harmonics in current spectrum; therefore, a new series of rotor harmonics is introduced to diagnose unbalanced supply. The locus of these harmonics is dependent on the poles, rotor bars, and slip. Due to the rapid complexity in industrial plants, it is inconceivable to continue human inspection to diagnose the faults. Thus, to avoid human inspection, in addition to new series of rotor harmonic, a fully automatic method based on neural network is proposed. This method not only diagnoses unbalanced voltage but it also recognize the percentage of unbalanced voltage by use of feed-forward multilayer perceptron (MLP) trained by back propagation. Finally, the experimental results shows the validation of this research work proposed method.

A new method for detection of unbalanced voltage supply through rotor harmonics and symbolic state dynamics

Induction motor is an extremely non-linear system and it poses a great challenge for the fault detection scheme due to large and complex data processing. Induction motor fault can lead to huge losses and excessive downtimes with regards to maintenance and production. An external fault like unbalanced voltage supply could be very sever and result in excessive losses, mechanical oscillations, overvoltage, and interference with control electronics. Detection of an abnormality like unbalanced voltage supply is a challenging task in the interaction of electrical motor and the power grid. In this paper, a new method is presented to diagnose unbalanced voltage supply at the incipient stage. In the proposed method, first of all, a new approach is introduced to formulate the total number of winding turns associated with a particular slot. After the formulation, the unbalanced voltage supply is diagnosed through signal processing, symbolic time series analysis and D-Markov state transition. The proposed method also distinguishes motor operation under balanced and unbalanced voltage supply. In the proposed work, hardware setup is designed for experimental verification. For validation of the method, an experimental setup is designed to justify and distinguish the motor operation under balanced and unbalanced voltage supply.

Effect of Unbalanced Voltage Supply Diagnosis Through Rotor Harmonics Signature and State Transitions

Induction motor is a workhorse in industrial system and it poses a great challenge for the fault detection scheme due to large and complex data processing. Induction motor fault can lead to huge losses and excessive downtimes with regards to maintenance and production. An external fault like unbalanced voltages supply could be much severed and result in excessive losses, mechanical oscillations, over-voltage, and interference with control electronics. Detection of an abnormality like unbalanced voltage supply is a challenging task in the interaction of electrical motor and the power grid. In this paper, two new methods are presented to diagnose unbalanced voltage supply at the incipient stage. In first method, a new approach is introduced to formulate the total number of winding turns associated with a particular slot. After the formulation, the unbalanced voltage supply was diagnosed through rotor harmonics based on the formulation. While in the other method, the unbalanced asymmetry was detected through signal processing, symbolic time series analysis and D-Markov state transition. The proposed methods also distinguish motor operation under balanced and unbalanced voltage supply. In the proposed work, hardware setup was designed for experimental verification. For validation of the methods, experimental setup was designed to justify and distinguish the motor operating under balanced and unbalanced voltage supply.

Resource Optimization Routing and Scatternet Formation Protocols for Bluetooth Networks

Limited resources and network dynamicity are challenging issues in Bluetooth networks. Therefore, scatternet formation improvement and efficient routing algorithms are required that can efficiently construct a short route between a source and destination. As of today, many protocols have been proposed, mostly researchers focusing on simplicity and reliability, but only few of them fulfill the Bluetooth scatternet scenario. Therefore, it opens new doors for scatternet formation and inter-piconet routing for the Bluetooth scatternet. This paper presents a review of Bluetooth routing and scatternet formation protocols for inter-piconet communication. First, Bluetooth operation is explained followed by important applications and topologies are discussed. Then, inter-piconet routing and network formation protocols are critically analyzed. Finally, Bluetooth problems and open research issues are highlighted.