The convergence of functionality between modular (e.g. VXI, PCI, PXI, LXI) and benchtop (conventional rack and stack) test and measurement instruments leads to decreased test costs and increased productivity for end users and their companies.Compared to benchtop instruments, the strengths of modular instruments are lower prices, easier system integration, and smaller footprints. In the past, however, most modular instruments have not provided the robust feature set and advanced analysis capabilities commonly found in benchtop instruments. Modular instrument usage is more widespread today as they become easier to use and offer many of the same advanced functions and features traditionally found in benchtop instruments.One key benefit of the modular/benchtop convergence is the emergence of "enterprise" instrumentation, where the same model of modular instrument hardware can be used in the design, validation, test, and even service sections of a company. It is possible to reconfigure soft front panel (SFP) applications to meet the needs of multiple users who may have dramatically different needs. For example, a design engineer may need an oscilloscope with historical memory to help with troubleshooting and may also need the ability to customize math waveform calculations. A validation engineer, not needing the advanced features required by the design engineer, might prefer a simplified user interface for basic testing. Likewise, a test engineer may only require a user interface for mask testing for simplified pass/fail determination. The ability of engineers or technicians in different sections of the company to use the same enterprise instrument results in decreased costs and increased productivity. Examples of cost savings include: decreased calibration and service costs; decreased training costs; and possibly quantity discounts from instrument vendors. An example of increased productivity is the use of consistent measurement algorithms at each stage in the design, validate, test, and service process, providing repeatable results.

This work presents a periodic preventive maintenance (PM) model for a repairable system that undergoes minimal repair or delayed repair at each failure to keep a plant operating. Two PM types are performed, i.e. imperfect PM and perfect PM. The likelihood that PM is perfect depends on the number of imperfect maintenance activities have been performed since the last renewal cycle. Mathematical formulae for expected cost per unit time are developed. The optimal period for periodic PM, which minimizes cost, is identified. Various special cases are considered, including the maintenance learning effect. A numerical example demonstrates the effectiveness of the proposed model.

In imperfect production processes, this paper considers production correction and maintenance to break away out of control state. Production processes are classified into two types of state: one is the type I state (out-of-control state) and the other is the type II state (in-control state). The type I state involves adjustment of the production mechanism. Production correction is either imperfect; worsening a production system, or perfect, returning it to "in-control" conditions. After N type I states, the operating system must be maintained and returned to the beginning condition. At the beginning of the production of the each renewal cycle, the state of the process is not always to be restored to "in-control". The total cost until "in-control" state, is determined. The existence of a unique and finite optimal N for an imperfect process under certain reasonable conditions is shown. A numerical example is presented.