Machine repairing systems with standby switching failure

The test cost and yield improvement of embedded memories have become very important as memory capacity and density have grown. For embedded memories, built-in redundancy analysis (BIRA) is widely used to improve yield by replacing faulty cells with a 2-D redundancy architecture. However, the most important factor in BIRA is the reduction of hardware overhead while keeping optimal repair rate. Most BIRA approaches require extra hardware overhead in order to store and analyze faults in the memory. These approaches do not utilize spare memories during the redundancy analysis (RA) procedure. However, the proposed BIRA minimizes area overhead by utilizing a part of the spare memory as an address mapping table (AMT). Since storing the faulty memory addresses take most of the extra hardware overhead, the reduced logical addresses produced by the AMT are used to reduce the extra hardware overhead. In addition, the reduced addresses are stored in content-addressable memories (CAMs) and used in the RA procedure. The proposed BIRA can achieve an optimal repair rate by using an exhaustive search RA algorithm. The proposed RA algorithm compares the repair solution candidates with all the fault addresses stored in the proposed CAMs to guarantee an exhaustive search. The experimental results show that the proposed BIRA requires a smaller area overhead than that of the previous state-of-the-art BIRA with an optimal repair rate.

With the growth of memory capacity and density, test cost and yield improvement are becoming more important. In the case of embedded memories for systems-on-a-chip (SOC), built-in redundancy analysis (BIRA) is widely used as a solution to solve quality and yield issues by replacing faulty cells with extra good cells. However, previous BIRA approaches focused mainly on embedded memories rather than commodity memories. Many BIRA approaches require extra hardware overhead to achieve the optimal repair rate, which means that 100% of solution detection is guaranteed for intrinsically repairable dies, or they suffer a loss of repair rate to minimize the hardware overhead. In order to achieve both low area overhead and optimal repair rate, a novel BIRA approach is proposed and it builds a line-based searching tree. The proposed BIRA minimizes the storage capacity requirements to store faulty address information by dropping all unnecessary faulty addresses for inherently repairable die. The proposed BIRA analyzes redundancies quickly and efficiently with optimal repair rate by using a selected fail count comparison algorithm. Experimental results show that the proposed BIRA achieves optimal repair rate, fast analysis speed, and nearly optimal repair solutions with a relatively small area overhead.

We presented a MX /G1, G2 /1 queueing system with a Bernoulli schedule that includes service disruption, working breakdown, balking, catastrophe and extended server vacations. All customers that arrive are served by a single server, with service time determined by general distribution. As soon as the system fails, the server keeps serving the current client at a reduced rate while repairs are made. It is believed that the vacation schedule is broad. Random disruptions to the server occur, with an exponential distribution in the length of the disruption. We assume that besides a Poisson stream of positive arrivals, there is a Poisson stream of negative arrivals, which we refer to as catastrophes. Using the supplementary variable technique, the Laplace transforms of the time-dependent probability of the system state are derived. We can infer steady-state outcomes from this. The average waiting time and queue size were also obtained. An additional topic of discussion is Adaptive Neuro-Fuzzy Inference system (ANFIS). Furthermore, this work uses Particle Swarm Optimisation (PSO), Artificial Bee Colonies (ABC), and Genetic Algorithms (GA) to swiftly find the system’s optimal cost. Additionally, we examined the convergence of several graph-optimisation strategies.

Modeling of machine interference problem with unreliable repairman and standbys imperfect switchover

A system with two subsystems working in alternating periods

Behaviour of a complex system with stand-by redundancy under different repair echelons

A geometrical process repair model for a repairable system with delayed repair

A repairable system with imperfect coverage and reboot

In this paper, a single server queueing model, wherein the units arrive in bulk with varying arrival rates in Poisson process, is considered. It is assumed that the service time of units is arbitrarily distributed. Also, we incorporate the optional deterministic vacations for the server. The server may take a vacation of a fixed duration at the completion of each service or may continue to be available in the system for the next service. At busy and vacation states, the customers may balk from the system with different balking probabilities. By using the basic assumptions of probability reasoning and supplementary variable technique, the steady state behaviour of the system is studied and various performance measures are obtained. In order to obtain the approximate values of the system state probabilities, the principle of maximum entropy is also employed. To verify the tractability of the performance measures obtained, the numerical illustrations are provided. Further, the sensitivity analysis is carried out to examine the system performance with respect to different parameters.

In this paper, we consider a continuous review perishable inventory system with multi-server service facility. In such systems the demanded item is delivered to the customer only after performing some service, such as assembly of parts or installation, etc. Compared to most inventory models in which inventory is depleted at the demand rate, it is depleted, in this model, at the rate that the service occurs. We assume that the arrivals of customers are according to a Markovian arrival process (MAP) and that service time has exponential distribution. The ordering policy is based on (s; S) policy. The lead time is assumed to have exponential distribution. The customer who finds either all servers are busy or no item (excluding those in service) is in the stock, enters into an orbit of infinite size. These orbiting customers send requests at random time points for possible selection of their demands. The interval time between two successive request-time points is assumed to have exponential distribution. In addition to the regular customers, a second flow of negative customers following an independent MAP is also considered which will remove one of the customers in the orbit. The joint probability distribution of the number of busy servers, the inventory level and the number of customers in the orbit is obtained in the steady state. Various measures of stationary system performance are computed and the total expected cost per unit time is calculated.

A multi-server perishable inventory system with negative customer

In this paper, we present an analysis of a machine repair problem with M operating machines and S standbys, in which c repairmen are responsible for supervising these machines and follow a multi-threshold, synchronous vacation policy. With such a policy, at a repair completion instant, if the number of the failed machines in the system is less than a preset threshold value, partial idle servers together take a single vacation (or leave for a random amount of time doing other secondary job). At the end of a vacation, they return to repair the failed machines (or waits for the arrival). The steady-state probabilities are solved by using the matrix-analytic method, and formulae of system performance measures are thereby obtained. A cost model is then developed to formulate an optimization problem to find the minimum cost. The direct search and quasi-Newton methods are implemented to determine the optimal number of servers, the optimal threshold policy and the optimal repair rate.

Test cost and yield enhancements are the vital parameters as the memory capacity and density increase. Built-in redundancy analysis (BIRA) is primarily used for incorporated memories to resolve yield and quality by replacing faulty cells with available cells. Various BIRA approaches need different area overheads to achieve optimal repairs. It is not easy to get low area overhead along with optimal repair rate. A novel BIRA method is proposed that requires less storage for storing faulty address information by deleting the data of unimportant faulty cell addresses. The proposed BIRA examines redundant spares quickly to achieve optimal repair rate. Simulation results show that the proposed BIRA does the spare allocations efficiently.

Availability of the system with general repair time distributions and shut-off rules

The purpose of this paper is to study the performance of a communication network system consisting of a transmitter, two relay stations and a receiver arranged in series parallel. Through the transition diagram, the partial differential equations are derived, and Laplace transforms are then taken on these equations to derive system reliability, availability, the mean time to system failure (MTTF) and cost function. It is assumed that failure rates are constant and follows exponential distribution, repair rates of partial failure state are assumed to follow general distribution and complete failure states are repaired through Gumbel-Hougaard family copula. The system is analyzed through supplementary variable technique and Laplace transform. Different measures of testing system effectiveness which include reliability, availability, mean time to failure (MTTF) and profit function have been calculated for particular values of time, failure and repair rates. From the study, it is clear that time and failure rates of both transmitter, relay stations and receiver influence the reliability, availability, MTTF and profit function. Mathematical models developed in this paper can aid plant management for proper maintenance and system safety, avoiding incorrect reliability, availability and profit assessment and leading to inadequate maintenance decision making, which may result in unnecessary expenditures and reduction of safety standards.