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Circuit Cellar's editorial team comprises professional engineers, technical editors, and digital media specialists. You can reach the Editorial Department at editorial@circuitcellar.com, @circuitcellar, and facebook.com/circuitcellar

Silicon Labs Acquires Micrium

Silicon Labs recently announced its acquisition of of Micrium, an RTOS software supplier. The strategic acquisition it intended to strengthen Silicon Labs’s position in the IoT market.

The following statement from Daniel Cooley, Senior Vice President and General Manager of Silicon Labs’s IoT products, was presented in a release:

IoT products are increasingly defined by software. Explosive growth of memory/processor capabilities in low-end embedded products is driving a greater need for RTOS software in connected device applications… The acquisition of Micrium means that connected device makers will have easier access to a proven embedded RTOS geared toward multiprotocol silicon, software and solutions from Silicon Labs.

Source: Silicon Labs

RMS Power Detector Offers High Accuracy Measurement

Linear Technology recently introduced the LTC5596, which is a high-frequency, wideband RMS power detector that provides accurate power measurement of RF and microwave signals independent of modulation and waveforms. It responds in an easy-to-use log-linear 29 mV/dB scale to signal levels from –37 to –2 dBm with accuracy better than ±1 dB error over the full operating temperature range and RF frequency range from 200 MHz to an unprecedented 30 GHz.Linear LTC5596

The LTC5596’s features and specs:

  • An extraordinarily wide bandwidth enables the detector to work seamlessly across multiple frequency bands using a common design with minimum calibration.
  • Operates from a single 3.3-V supply, drawing a nominal supply current of 30 mA.
  • Built-in improved ESD protection.
  • Two temperature grades: an I grade is designed for operation from –40° to 105°C case. A high-temperature H-grade has rated temperature from –40° to 125°C case.
  • Both temperature versions are available in a 2 mm × 2 mm plastic eight-lead DFN package.

The LTC5596 I-grade starts at $12.50 each in 1,000-piece quantities. The H-grade starts at $16.95 each. Both versions are available in production quantities.

Source: Linear Technology

October Code Challenge (Sponsor: Programming Research)

Ready to put your programming skills to the test? Take the new Electrical Engineering Challenge (sponsored by Programming Research). Find the error in the code for a shot to win prizes, such as an Amazon Gift Card, a Circuit Cellar magazine digital subscription, or a discount to the Circuit Cellar webshop.

The following program will compile with no errors. It runs and completes with no errors.

Click to enlarge. Find the error and submit your answer via the online submission form below. Submission deadline: 2 PM EST, October 20.

Take the challenge now!

Flowcode 7 (Part 1): Simplifying Microcontroller Programming (Sponsor: Matrix)

These days the most commonly used device in electronic systems is the microcontroller: it is hard to find a piece of electronics without one, and you use thousands of them a day. In this free article, John Dobson, managing director at Matrix TSL, introduces Flowcode 7 and explains how you can use it for your next microcontroller-based design.

Want a Free Trial and/or Buy Flowcode 7? Download Now

Flowcode is an IDE for electronic and electromechanical system development. Pro engineers, electronics enthusiasts, and academics can use Flowcode to develop systems for control and measurement based on microcontrollers or on rugged industrial interfaces using Windows-compatible personal computers. Visit www.flowcode.co.uk/circuitcellar to learn about Flowcode 7. You can access a free version, or you can purchase advanced features and professional Flowcode licenses through the modular licensing system. If you make a purchase through that page, Circuit Cellar will receive a commission.

Click to download the article

Click to download the article

The Future of Biomedical Signal Analysis Technology

Biomedical signals obtained from the human body can be beneficial in a variety of scenarios in a healthcare setting. For example, physicians can use the noninvasive sensing, recording, and processing of a heart’s electrical activity in the form of electrocardiograms (ECGs) to help make informed decisions about a patient’s cardiovascular health. A typical biomedical signal acquisition system will consist of sensors, preamplifiers, filters, analog-to-digital conversion, processing and analysis using computers, and the visual display of the outputs. Given the digital nature of these signals, intelligent methods and computer algorithms can be developed for analysis of the signals. Such processing and analysis of signals might involve the removal of instrumentation noise, power line interference, and any artifacts that act as interference to the signal of interest. The analysis can be further enhanced into a computer-aided decision-making tool by incorporating digital signal processing methods and algorithms for feature extraction and pattern analysis. In many cases, the pattern analysis module is developed to reveal hidden parameters of clinical interest, and thereby improve the diagnostic and monitoring of clinical events.Figure1

The methods used for biomedical signal processing can be categorized into five generations. In the first generation, the techniques developed in the 1970s and 1980s were based on time-domain approaches for event analysis (e.g., using time-domain correlation approaches to detect arrhythmic events from ECGs). In the second generation, with the implementation of the Fast Fourier Transform (FFT) technique, many spectral domain approaches were developed to get a better representation of the biomedical signals for analysis. For example, the coherence analysis of the spectra of brain waves also known as electroencephalogram (EEG) signals have provided an enhanced understanding of certain neurological disorders, such as epilepsy. During the 1980s and 1990s, the third generation of techniques was developed to handle the time-varying dynamical behavior of biomedical signals (e.g., the characteristics of polysomnographic (PSG) signals recorded during sleep possess time-varying properties reflecting the subject’s different sleep stages). In these cases, Fourier-based techniques cannot be optimally used because by definition Fourier provides only the spectral information and doesn’t provide a time-varying representation of signals. Therefore, the third-generation algorithms were developed to process the biomedical signals to provide a time-varying representation, and   clinical events can be temporally localized for many practical applications.

This essay appears in Circuit Cellar 315, October 2016. Subscribe to Circuit Cellar to read project articles, essays, interviews, and tutorials every month!

These algorithms were essentially developed for speech signals for telecommunications applications, and they were adapted and modified for biomedical applications. The nearby figure illustrates an example of knee vibration signal obtained from two different knee joints, their spectra, and joint time-frequency representations. With the advancement in computing technologies, for the past 15 years, many algorithms have been developed for machine learning and building intelligent systems. Therefore, the fourth generation of biomedical signal analysis involved the automatic quantification, classification, and recognition of time-varying biomedical signals by using advanced signal-processing concepts from time-frequency theory, neural networks, and nonlinear theory.

During the last five years, we’ve witnessed advancements in sensor technologies, wireless technologies, and material science. The development of wearable and ingestible electronic sensors mark the fifth generation of biomedical signal analysis. And as the Internet of Things (IoT) framework develops further, new opportunities will open up in the healthcare domain. For instance, the continuous and long-term monitoring of biomedical signals will soon become a reality. In addition, Internet-connected health applications will impact healthcare delivery in many positive ways. For example, it will become increasingly effective and advantageous to monitor elderly and chronically ill patients in their homes rather than hospitals.

These technological innovations will provide great opportunities for engineers to design devices from a systems perspective by taking into account patient safety, low power requirements, interoperability, and performance requirements. It will also provide computer and data scientists with a huge amount of data with variable characteristics.

The future of biomedical signal analysis looks very promising. We can expect  innovative healthcare solutions that will improve everyone’s quality of life.

Sridhar (Sri) Krishnan earned a BE degree in Electronics and Communication Engineering at Anna University in Madras, India. He earned MSc and PhD degrees in Electrical and Computer Engineering at the University of Calgary. Sri is a Professor of Electrical and Computer Engineering at Ryerson University in Toronto, Ontario, Canada, and he holds the Canada Research Chair position in Biomedical Signal Analysis. Since July 2011, Sri has been an Associate Dean (Research and Development) for the Faculty of Engineering and Architectural Science. He is also the Founding Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST). He is an Affiliate Scientist at the Keenan Research Centre at St. Michael’s Hospital in Toronto.

SuperFET III Family of 650-V N-channel MOSFETs

Fairchild Semiconductor recently introduced the SuperFET III family of 650-V N-channel MOSFETs, which are well suited for telecom equipment, electric vehicle (EV) chargers, solar products, and more. The SuperFET III MOSFET family combines reliability, low EMI, high efficiency, and superior thermal performance. Furthermore, its various package options give you greater flexibility when dealing with space-constrained designs.

The SuperFET III has the lowest Rdson in any easy drive version of a Super Junction MOSFET. It is has 3× better single pulse Avalanche Energy (EAS) performance than its closest competitor. Such advantages make it useful for industrial applications such as solar inverters and EV chargers.

The SuperFET III MOSFET family is now available in multiple package and parametric options.

Source: Fairchild Semiconductor

40-VIN, 2.1-A Rail-to-Rail LDO+ Available in High-Temperature, 150°C H-Grade in TSSOP Package

Linear Technology Corp. recently unveiled a new higher temperature “H-grade” version of the LT3086 in the TSSOP package. The 40-V, 2.1-A low dropout linear regulator (LDO) includes current monitoring with externally settable current limit and temperature monitoring with external control of thermal limit temperature. It comprises a programmable power good status flag, cable drop compensation, and easy paralleling. The current reference provides regulation independent of output voltage.LTC3086 image

 

The LT3086’s features and specifications include:

  • –40°C to 150°C (H-Grade TSSOP Only) Operating Junction Temperature Range
  • 1.4 to 40 V Input Voltage Range
  • One Resistor Sets Output Voltage: 0.4 to 32 V
  • Output Current: 2.1 A
  • ±2% Tolerance Over Line, Load and Temperature
  • Output Current Monitor: IMON = IOUT/1000
  • Temperature Monitor with Programmable Thermal Limit
  • Programmable Current Limit
  • Programmable Cable Drop Compensation
  • Parallel Multiple Devices for Higher Current
  • Dropout Voltage: 330 mV
  • One Capacitor Soft-Starts Output and Decreases Noise
  • Low Output Noise: 40 μVRMS (10 Hz to 100 kHz)
  • Precision, Programmable External Current Limit
  • Power Good Flag with Programmable Threshold
  • Ceramic Output Capacitors: 10 μF Minimum
  • Quiescent Current in Shutdown: less than 1 μA
  • Reverse-Battery, Reverse-Current Protection
  • Available in 4 mm × 5 mm 16-Lead DFN, 16-Lead TSSOP, Seven-Lead DD-PAK and Seven-Lead TO-220 Packages

Source: Linear Technology

Isolated FET Driver for Industrial Relay Replacement Applications

Silicon Labs recently introduced a new CMOS-based isolated field effect transistor (FET) driver family for industrial and automotive applications. The family enables you to use your preferred application-specific, high-volume FETs to replace old electromechanical relays (EMRs) and optocoupler-based, solid-state relays (SSRs).Si875x Silicon Labs

The new Si875x family features the industry’s first isolated FET drivers designed to transfer power across an integrated CMOS isolation barrier. When paired with a discrete FET, the Si875x drivers provide a superb EMR/SSR replacement solution for motor and valve controllers, HVAC relays, battery monitoring, and a variety of other applications.

The Si875x isolated FET driver family’s features and specs:

  • Industry’s first CMOS isolation-based SSR solution, supporting application-specific FETs
  • Best-in-class noise immunity, high reliability and 2.5 kVRMS isolation rating
  • Long lifetimes under high-voltage conditions (100 years at 1000 V)
  • Efficient switching: 10.3 V at the gate with only 1 mA of input current
  • Wide input voltage of 2.25 to 5.5 V enables power savings
  • Unique pin feature optimizes power consumption/switching time trade-off
  • Miller clamping prevents unintended turn on of external FET
  • Small SOIC-8 package integrates isolation and power capacitors for low-power applications
  • AEC-Q100-qualified automotive-grade device options

The Si875x devices come in a small SOIC-8 package. They are available in both industrial (–40°C to 105°C) or automotive (–40°C to 125°C) ambient temperature operating range options. Pricing in 10,000-unit quantities begins at $0.96 for industrial versions and $1.20 for automotive temperature options.

Evaluation kits are available. The Si8751-KIT (digital input) and Si8752-KIT (LED emulator input) evaluation kits cost $39.99 each.

Source: Silicon Labs

ON Semiconductor Acquires Fairchild Semiconductor

ON Semiconductor recently acquired Fairchild Semiconductor for $2.4 billion. Fairchild develops semiconductor solutions for mobile and power designs.

“The acquisition of Fairchild is a transformative step in our quest to become the premier supplier of power management and analog semiconductor solutions for a wide range of applications and end-markets,” said Keith Jackson, president and CEO of ON Semiconductor, in a press statement. “Fairchild provides us a plat-form to aggressively expand our profitability in a highly fragmented industry. With the addition of Fairchild, our industry leading cost structure has further improved in a significant manner and we are now well positioned to generate substantial shareholder value as we integrate operations of the two companies.”

Source: ON Semiconductor

Boldport Club: Behind the Scenes

We first met London-based engineer Saar Drimer in December 2015. At that time, his was running Boldport—a hardware and prototyping consultancy that specializes in circuit boards—from a workspace was in one of the characteristic arches underneath London Bridge Station. A lot has changed since then. Today, Drimer has a new workspace and he is running Boldport Club, which is a monthly electronics hardware subscription service. We recently met up with him to discuss his work and newest endeavors.

“The big change is the club I started early this year,” Drimer explained. “I posted my initial ideas online and the response was very promising, around 170 members signed up in the first month.”

Ultra-Compact Bluetooth 4.2 + NFC Module

Rigado’s new BMD-350 Bluetooth 4.2 + NFC module is intended for use in Internet of Things (IoT) applications. With  8.6 × 6.4 × 1.5 mm footprint and based on the Nordic Semiconductors nRF52 series SoC, the BMD-350 gives IoT innovators a “plug-and-play” connectivity solution perfectly suited for high-performance, low-power wearables and portable devices. The Nordic Semiconductors nRF52 series brings on-chip NFC capability for new modes in IoT pairing. Both the BMD-350 and the BMD-350 evaluation kit are now available.

Source: Rigado

IAR Embedded Workbench for ARM Supports IoT-Targeted MCUs

IAR Systems recently announced that IAR Embedded Workbench for ARM now supports microcontrollers based on ARM Cortex-M3/M4 and ARM Cortex-A15 that are targeted for connectivity and the Internet of Things (IoT).

IAR Embedded Workbench for ARM is a complete C/C++ compiler and debugger toolchain for developing embedded applications. The toolchain generates efficient code, which makes it well suited for developing energy-efficient, time-critical IoT applications.

Because the IAR Embedded Workbench for ARM toolchain is continuously updated with new microcontroller support, you are free from having to consider the choice of software in your selection of a microcontroller. Instead of using different tools for different microcontrollers, you can use the same toolchain from start to finish. IAR Embedded Workbench for ARM is available in several versions, including a product package for the ARM Cortex-M core family.

Source: IAR Systems

IEEE 802.3bt PD Controller

Linear Technology Corp. recently introduced the LT4295IEEE 802.3bt powered device (PD) interface controller for applications that require up to 71 W. The next Power over Ethernet (PoE) standard—IEEE 802.3bt—enables manufacturers to go beyond the 25.5 W allocated by the 2009 IEEE 802.3at standard. The new standard—PoE++ or 4PPoE—increases the power budget to enable new applications and features, while supporting 10GBASE-T and maintaining backward compatibility with older IEEE equipment. The LT4295 is IEEE 802.3bt (Draft 2.0) compliant and supports newly introduced features, including all additional PD classes (5, 6, 7, and 8), additional PD types (Type 3 and Type 4), and five-event classification.Linear-LT4295

The LT4295 is a single-signature 802.3bt PD controller that integrates an isolated switching regulator controller. It is capable of synchronous operation in both high-efficiency forward and no-opto flyback topologies with auxiliary power support. This simplifies front end PD designs by reducing component count and board space, which means the LT4295 can deliver power to PD loads using just one IC. Unlike traditional PD controllers, the LT4295 controls an external MOSFET to reduce overall PD heat dissipation and maximize power efficiency. You can size the MOSFET to your application’s requirements. Standard LT4295-based implementations routinely select 30-mΩ RDS(ON) MOSFETs.

The LT4295’s features and specs:

  • IEEE 802.3af/at/bt (Draft 2.0) Powered Device (PD) with Forward/Flyback Controller
  • External hot swap N-channel MOSFET for lowest power dissipation and highest system efficiency
  • Supports up to 71-W PDs
  • Five-event classification sensing
  • Superior surge protection (100-V absolute maximum)
  • Wide junction temperature range (–40° to 125°C)
  • 94% End-to-end efficiency with LT4321 ideal bridge
  • No-opto flyback operation
  • Auxiliary power support as low as 9 V
  • Available in 28-lead 4 mm × 5 mm QFN package

Source: Linear Technology

Electrical Engineering Crossword (Issue 315)

315 crossword grid answerAcross

  1. SPECTROMETER—Device for measuring wavelengths
  2. KILLSWITCH—Big Red Switch [two words]
  3. RECURSION—When a routine calls itself
  4. BOOLEAN—Two values: true and false
  5. PASCAL—Pa
  6. TERA—1012
  7. FETCH—Load data before execution
  8. HECTO—102
  9. TRIODE—Amp device with three electrodes
  10. DUB—Copy

Down

  1. CYBERNETIC—What’s cyber short for?
  2. VISHING—Phishing via phones
  3. MEMRISTOR—Memory resistor
  4. HANDSHAKE—Signal exchange of that starts or ends a function
  5. TRANSFORMER—Power brick
  6. BALUN—Transformer used to convert balanced/unbalanced signals
  7. EXBIBYTE—1,152,921,504,606,846,976 bytes
  8. QUBIT—Quantum bit
  9. GANGED—Coupled to work together (e.g., potentiometers)
  10. STEP—An increment

Tips for Predicting Product Reliability

British Prime Minister Benjamin Disraeli (1804–1881) once uttered: “There are lies, damned lies, and statistics.” I don’t say statistics lie, but not everything presented to us as a result of statistical analysis is necessarily true. Statistics are instrumental for investigation of many scientific and social subjects, but they’re just a tool. Incorrectly used, their results can be wittingly or unwittingly skewed and potentially used to prove or disprove just about anything.

One use of statistics in engineering is to predict product reliability, a topic I’ve addressed in several of my previous columns. In this article, I’ll investigate it further.

PREDICTED RELIABILITY

Predicted reliability is a probability of a product functioning without a failure for a given length of time. It is usually presented as a failure rate λ (Greek letter lambda) indicating the probable number of failures per million hours of operation. Its reciprocal Mean Time Between Failures (MTBF) or Mean Time to Failure (MTTF for irreparable products) is often preferred because it is easier to comprehend.

Having determined a product’s reliability, we can establish its criticality, the likely warranty and maintenance costs as well as to plan repair activities with spare parts quantities and their allocation. Figure 1 is the ubiquitous reliability bath tub curve. The subject of our discussion is the period called “Constant Failure Rate.”

Figure 1: Reliability bath tub curve.

Figure 1: Reliability bath tub curve.

Predicted reliability is not a precise number. It is a probability with less than 100% certainty that the product will work for the specified time. Unfortunately, many buyers, program managers, even design engineers do not recognize this and expect the predicted failure rate to be a certainty.

Most engineers aren’t expert statisticians, nor can every design organization afford such specialists on staff. Historical data to perform reliability prediction are rarely adequate in small companies. Luckily, the US military made the reliability prediction easy to calculate by following their Military Handbook: Reliability of Electronic Equipment (MIL-HDBK-217), now in version F (www.sre.org/pubs/Mil-Hdbk-217F.pdf), based on data collected over many years.

The general method for calculating failure rate during the development stages is by parts count which, once all design details are known, is refined by parts’ stress analysis.

With the parts count method, the predicted failure rate is:Novacek 314 EQ1

In this equation, λEQUIP is the total equipment failures per million hours. λg is the generic failure rate for ith generic part. πQ is the quality factor for ith generic part. Ni quantity of ith generic part. n is the number of different generic part categories in the equipment. Using a spreadsheet, for example, you follow the Military Handbook by calculating failure rates for individual components.

A typical component failure rate model is:Novacek 314 EQ2

In this equation, λp is the part’s failure rate. λb is the part’s base failure rate. p are factors modifying the base rate for stress, application, environment, and so on.

The Military Handbook provides tables with statistical data for components’ base failure rate and all the pertinent p factors. It is one of several methods for calculating predicted reliability. I was interested to see how some different statistical methods compare with each other. For my inquiry I selected an Arduino Uno, which many readers are familiar with (see Photo 1). Not having the Arduino’s design details, I estimated operating conditions. Because I used the same estimates for all the calculations, the relative comparison of the results is valid, while the absolute value may be somewhat off.

Photo 1: Arduino Uno

Photo 1: Arduino Uno

I have a number of Arduino boards, Duemilanove and Uno, which are quite similar, and through the years have collectively clocked over 600,000 hours of continuous operation without a single failure (including all their peripheral circuits which are not, however, included in my calculations). Several of my Arduinos have worked in the garage with the recorded ambient temperature ranging from –32°C (–25.6°F) to 39°C (102.2°F), which falls under the military category “ground fixed” (GF) environment. I used this for my calculations. While components’ failure rates generally increase exponentially with temperature, operation below about 50°C (122°F) does not significantly increase the stress over a room temperature, so there would not be a significant difference in failure rate between those Arduinos working in the garage and those inside the house.

CALCULATIONS

I performed two manual calculations according to MIL-HDBK-217F and one per HDBK 217 Plus using Mathcad. The failure rate of Item 1 in Table 1 was calculated per MIL-HDBK-217F for commercial parts. The MIL handbook has been criticized for two shortcomings. First, for the unrealistic penalty on commercial parts by today’s quality standards as compared to the military-screened parts. This is understandable in view of the second shortcoming: that the handbook has not been updated since 1995. At that time, component manufacturers discontinued screening their parts for military compliance, but simultaneously and up to the present, have been improving quality of manufacturing processes, thus increasing components’ reliability. Based on experience, I modified the components’ quality factors and the result is shown by Item 2.

Table 1: Calculated reliability of Arduino Uno

Table 1: Calculated reliability of Arduino Uno

There are a number of commercially available programs to aid in reliability prediction calculations. You only need to input the bill of material (BOM) with the components’ operating specifics, such as the temperature, derating, and so forth. The programs have built-in databases and do the calculations for you. Those programs are powerful, and in addition to the predicted failure rate, they can generate analyses such as Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and others. Sadly, they are not inexpensive.

As there are different methodologies for calculating the predicted failure rate, these programs allow you to select which methodology you want to use. I performed 10 calculations for the same Arduino BOM and operating conditions by three computer programs per MIL-HDBK-217F, HDBK 217 Plus, Bellcore, Telcordia, Siemens, PRISM (which I believe is based on HDBK 217 Plus), and FIDES 2009 methodologies.

The results fall into two fairly consistent groups, roughly an order of magnitude (discounting Item 1) apart. Table 1 lists the calculated results and Figure 2 its graphic representation. Figure 2 displays the MTBF rather than the failure rate, which is easier to visualize.

Figure 2: Results tabulated in Table 1 shown graphically output.

Figure 2: Results tabulated in Table 1 shown graphically output.

As I already said, I discounted Item 1 which only demonstrates the MIL-HDBK-217F obsolete bias towards commercial parts. Items 3 and 4 show that the MIL-HDBK-217F implementation by commercial programs and my own adjustment, Item 2, are reasonably close. So are items 8, 10, and 13. These methodologies are geared towards tough military/aerospace applications and, therefore, I suspect, their statistical treatment is more conservative than that of Items 5, 6, 7, 11, and 12, which show the predicted MTBF up to more than a decade greater.

We should remind ourselves what those MTBF numbers mean. One hundred thousand hours MTBF represent 11.5 years of continuous operation! That’s a long time. It would be 23 years if operated only 12 hours daily. Many products become obsolete or fall apart before then. Consequently, I am rather skeptical about the predicted 578 years MTBF of Item #11.

Bellcore methodology was developed for telephone equipment, FIDES 2009 is the result of the efforts of the European aerospace manufacturers and the results are close to MIL-HDBK-217F. HDBK 217 Plus, Telcordia, Siemens and PRISM provided results by an order of magnitude greater.

It is important to strive for a realistic predicted failure rate because other analyses, not to mention design and manufacturing costs, warranty and spare parts allocation are affected by it. Later refinements of the calculated prediction by stress analyses, reliability testing and especially field experience should bring us close to the real value.

CHOOSE A METHOD

Which methodology should you use? My customers have always required MIL-HDBK-217, so I never had the headache of having to make and then justify my own choice. Despite its age, MIL-HDBK-217 continues to be alive and well to this day in the aerospace and military industries. In comparison with the results of Items 5, 6, 7, 9, 11, 12 MIL-HDBK-217 seems rather conservative, but when it comes to safety critical designs I much rather err on the safe side. My experience with the Arduinos doesn’t provide sufficient data to draw a general conclusion, although it appears to be better than the MIL-HDBK-217 predicts.

Ultimately, the field results will be the only thing that counts. All the calculations will be meaningless if the product’s reliability in field is not as required by the customer’s specification. Then you will be called upon to fix the design or the manufacturing process or both.

Reliability calculations differ from methodology to methodology, but if you are consistent using the same methodology, those numbers, coupled with experience, will enable you to judge what the field results will be. All my MTBF calculations met the specifications, but the field results have always exceeded those calculations. And that’s what really counts in the end.

George Novacek is a professional engineer with a degree in Cybernetics and Closed-Loop Control. Now retired, he was most recently president of a multinational manufacturer for embedded control systems for aerospace applications. George wrote 26 feature articles for Circuit Cellar between 1999 and 2004. Contact him at gnovacek@nexicom.net with “Circuit Cellar” in the subject line.