there are lots of ways to increase a motor’s performance but only one way to quantify its performance: by checking it on a dynamometer (dyno, for short). A motor dyno can shed light on why you get smoked on the straight or your motor overheats, and it lets you record your motors “likenew” power output so you’ll later be able to see how this has changed with use. Unfortunately, dynos are expensive; you can pay close to $i,ooo for a good one. A new dyno-the PowerCheck from CS electronic (imported by Schumacher USA*), has a long list of features and costs about half as much as similar units. Does it work as promised? I’m glad to say it does; here’s what you getFEATURES I Inertial testing. To determine a motor’s torque and power, the dyno uses calculations based on the flywheel’s inertia. To greatly simplify things, the dyno measures how long it takes the motor to accelerate the flywheel to maximum rpm, then it calculates-using acceleration time, amp draw and voltage figures-to determine thi motor’s torque, power in watts, efficiency and other performance parameters. Battery-powered motor operation. The PowerCheck operates off a uV power supply and requires a 6-cell Ni-Cd pack to power the test motor. By using an RC battery to power the motor, CS reduces the cost of the PowerCheck by eliminating the need for an expensive internal power supply that can handle the high-amp draw of modified motors. The only downside is a certain inconsistency in results when you test a motor over a long period. Since battery-pack performance changes with age and use, the numbers you get on a motor today may not be directly comparable to those you obtain a few months down the road if you power the dyno with a different battery. Even if you use the same battery for the retest, your results will be skewed if that pack has also seen active race duty since it was first used to power the dyno. But most motor builders are interested in single-session numbers and not in long-term tests, so this is a minor problem. If you can spare a pack exclusively for use in the dyno, that’s your best bet.

Daily variations in ambient particulate air pollution are associated with variations in respiratory lung function. It has been suggested that the effects of particulate matter may be due to particles in the ultrafine (0.01-0.1 [micro]m) size range. Because previous studies on ultrafine particles only used self-monitored peak expiratory flow rate (PEFR), we assessed the associations between particle mass and number concentrations in several size ranges measured at a central site and measured (biweekly) spirometric lung function among a group of 54 adult asthmatics (n = 495 measurements). We also compared results to daily morning, afternoon, and evening PEFR measurements done at home (n = 7,672-8,110 measurements). The median (maximum) 24 hr number concentrations were 14,500/[cm.sup.3] (46,500/[cm.sup.3]) ultrafine particles and 800/[cm.sup.3] (2,800/[cm.sup.3]) accumulation mode (0.1-1 [micro]m) particles. The median (maximum) mass concentration of [PM.sub.2.5] (particulate matter [is less than] 2.5 [micro]m) and [PM.sub.10] (particulate matter [is less than] 10 [micro]m in aerodynamic diameter) were 8.4 [micro]g/[m.sup.3] (38.3 [micro]g/[m.sup.3]) and 13.5 [micro]g/[m.sup.3] (73.7 [micro]g/[m.sup.3]), respectively. The number of accumulation mode particles was consistently inversely associated with PEFR in spirometry. Inverse, but nonsignificant, associations were observed with ultrafine particles, and no associations were observed with large particles ([PM.sub.10]). Compared to the effect estimates for self-monitored PEFR, the effect estimates for spirometric PEFR tended to be larger. The standard errors were also larger, probably due to the lower number of spirometric measurements. The present results support the need to monitor the particle number and size

So you’ve found an alternative therapy you’d like to try in addition to your doctor’s current recommendations. Maybe you’ve even gone ahead and started a complementary treatment.

Of course, you should tell your doctor, right?

But somehow, you forgot to bring it up. You’re anything but alone in your reluctance. According to a 1999 study performed by the University of California at San Francisco’s Department of Anthropology, History and Social Medicine, the vast majority of breast cancer patients make use of complementary therapies but don’t discuss them with their conventional physicians

Why all the secrecy? The patients cited their doctors’ disinterest and inability to contribute useful information as leading reasons why they kept their complementary treatments to themselves.

In the seven or so minutes the average doctor gives each patient, it’s hard enough to even get adequate information about a standard approach to your condition.

For instance, a 1999 analysis of more than 1,000 audio-taped, doctor-patient interviews revealed that the discussion of basic information about standard treatments–such as side effects–almost never occurred, so expecting enthusiastic chitchat about alternative therapies that could address the cause of your health care options is even more unrealistic.

By their very nature, manufacturing operations are resistant to fundamental changes in production strategies. When reconfiguring a high-volume production process, therefore, the consequences of miscalculation are momentous enough to give managers the sensation of climbing onto a lofty tree limb. With gazes fixed on the ground below, the players in this scenario realize that they’re working without a safety net.

It’s tough to pinpoint spindle error. It may be caused by positioning error, and it may be a consistent or an inconsistent error Sometimes it’s linked to spindle speed. When manufacturing engineers at Cummins Engine Co. (Columbus, IN) wanted to find out how good a job was done on recently rebuilt machine tools, they decided to use an SEA 2.30 Spindle Error Analyzer from Federal Products Co. (Providence, RI).

This analyzer, which works on spindles operating at speeds up to 120,000 rpm, uses capacitance gages to measure the volume of air between the gage and a precision ball or mandrel mounted on the machine’s toolholder. As the spindle rotates, position variations are detected, amplified, and fed to the computer for analysis. This type of system is specified for use in CNC machine tool calibration under the ANSI/ASME B5.54 standard.

In the case of the rebuilt machine tool, they were able to detect a serious error caused by improperly loaded spindle bearings. After analysis and repair, asynchronous radial error at 2600 rpm was reduced 45% from 469 to 247 mu-in. (12.0 to. 6.3 mu-m) and radial error motion 43% from 150 to 86 pin. (3.8 to 2.2 mu-m).

Cummins now uses the Federal system to monitor all 50 CNC machine tools in its facility. In addition to detecting the need for immediate repair, the gage will also track performance changes and help schedule maintenance. Data obtained will also help managers assign jobs to machines based on required accuracy. For more information Circle 229.

The major role of temperature sensors in a machine tool thermal error compensation system is the improvement of machining accuracy through the supply of reliable temperature data on the machine structure. This paper presents a new method for the fault diagnosis of temperature sensors along with the recovery of faulty data, thereby protecting the reliability of a thermal error compensation system. The proposed method of detecting a fault and its location is based on the correlation coefficients among the temperature data produced by the sensors. Thereafter, a multiple linear regression model, prepared using the complete normal data, is used for the recovery of faulty data. The effectiveness of this method was tested by comparing computer simulation results with measured data from a CNC machining center.

Keywords: Thermal Error Compensation System, Fault Diagnosis, Recovery, Correlation Coefficient, Multiple Linear Regression Model

Introduction

The major role of temperature sensors in a machine tool thermal error compensation system is the improvement of machining accuracy through the supply of reliable temperature data for a thermal error model of the machine structure. However, contrary to previous expectations, malfunctions or faults in temperature sensors during the working of a compensation system can in fact increase the machining inaccuracy.1,2 Whereas the intuitive detection of faults in temperature sensors is very difficult during machining, there is also a serious financial implication to attaching an additional monitoring device for detecting sensor faults through an acceptance or rejection test of finished workpieces.

Accordingly, a fault detection and recovery technique for temperature sensors is necessary to improve the reliability of a thermal error compensation system. Plus, such a technique is also crucial to produce stable machining, prevent accidents, and improve productivity.

Previous research on the detection of system malfunctions and faults has been characterized by the target system; therefore, an influence diagram,3,4 artificial neural network,5 fuzzy algorithm,6,7 and statistical technique8 have all been applied.

The most serious fault patterns in a thermal error compensation system for CNC machine tools are the breaking of wires and the inclusion of noises. The lack of independence between the temperature sensor outputs is one of the basic characteristics of temperature sensors because the temperature variation in a machine structure is highly correlated.

This paper proposes a new method for fault diagnosis and the recovery of faulty sensor output in a thermal error compensation system. This method considers the characteristics of a thermal error compensation system for CNC machine tools. The correlation coefficients from the temperature data produced by the sensors are used for the fault diagnosis. The fault and its location are detected using a combination of a statistical hypothesis and test on the correlation coefficients. Thereafter, a multiple linear regression model, prepared using the complete normal data, is used for the recovery of faulty data.

The effectiveness of this method was tested by comparing computer simulation results with measured data from a CNC machining center.

Fault Diagnosis and Recovery Algorithm

Quantification of Correlation Among Temperature Variables

Because the temperature data from sensors in a thermal error compensation system can be prescribed as random variables, they are quantified as correlation coefficients. If the statistical correlation between temperature variables is quantified by correlation coefficients, there are several advantages.

First, the cause-and-effect relation between temperature variables can be converted to a simplified input-output relation. Therefore, the complicated physical relation between temperature variables need not be considered.

Second, the correlation coefficients for all the temperature variables can be easily obtained using a simple calculation.

Third, because the cause-and-effect relation between one variable and other variables is consistently determined, the location of a faulty sensor can be easily detected.