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Eine Befragung von 1' Führungskräften aus verschiedenen Unternehmen der Vereinigten Staaten zeigt, dass mit zunehmender Hierarchiestufe eines Feedback-Nehmers das Ausmass der Abweichung zwischen beruflicher Selbst- und Fremdeinschätzung ansteigt. Führungskräfte auf oberen Hierarchiestufen neigen eher zu einer Selbstüberschätzung als ihre Berufskollegen auf unteren Ebenen. Zu ergänzenden Ergebnissen kommen die Verfasser bei der Analyse ihres Datenmaterials aus der Durchführung von Feedbacks bei insgesamt beurteilten Führungskräften aus 9 Dienstleistungs- und Industrieunternehmen mit jeweils mindestens 60 Mitarbeitenden.

Eine Anwendung des Instruments bei Führungskräften ist aus empirischer Sicht sinnvoll. Es führt zu aufschlussreichen Ergebnissen, die den beurteilten Personen wichtige Erkenntnisse über die Urteilsdifferenz liefern. Diese stellt einen wichtigen Schritt zur Definition von Entwicklungsmassnahmen dar. Top-Down-Einschätzung von Leistungsverhalten und Kommunikation gegen oben. Ihnen fällt die Rolle des direkten Entwicklers und Coaches zu.

Als gleichberechtigte Partner im Team beurteilen sie unter anderem die Zusammenarbeit und das Fachwissen, da sie dies sehr nahe und unmittelbar erleben. Sie beurteilen Aspekte wie etwa die Servicequalität. Sie geben ein Feedback über das Führungsverhalten, das den Vorgesetzten die Möglichkeit zur Reflektion und Weiterentwicklung der Führungskompetenzen gibt.

Differenzierung Systemische Personalentwicklung Interne Beratung von mittleren und unteren Führungskräften 5. Mehrfachnennungen möglich 3 Umsetzung von Coaching im Unternehmen 3.

Führung Fringe Benfits Kommunikation finanzielle Anreize intrinsische Motivation kann erfolgen durch: Schlüsselkompetenzen werden erst durch ihre Anwendung in mehreren Kontexten sichtbar. Verwendete Begriffe zeigen auf, worauf es im Unternehmen, auf dem Arbeitsmarkt ankommt.

Abnehmende Bedeutung der Erstausbildung, häufigere Arbeitsplatzwechsel, Auftreten von Brüchen, Veränderung der bestehenden Arbeitsformen etc. Vorbereitungsprogramm als NachfolgekandidatIn für die Übernahme einer Schüsselposition z. Aufbau von Vertrauen durch zunehmende Führungskompetenz und abnehmender funktionaler Expertise Beispiel Kernkompetenzen: Zunehmend bereichs- und funktionsübergreifendes Denken und Handeln zwecks Beeinflussung der Gesamtorganisation Beispiel Kernkompetenzen: Ist permanent lernbereit 3.

Passt sich kulturellen Unterschieden an 4. Entwickelt ein breites Business Know-How 5. Bringt das Beste aus seinen Mitarbeitenden heraus 6. Ist einsichtig, sieht Sachen aus unterschiedlichen Perspektiven 7. Zeigt Mut zum Risiko 8. Sucht und gibt Feedback 9. Lernt aus Fehlern RSM Managers are also ranked and will occupy a quartile based on the combination of their technical contributions and their people management responsibilities.

Erreicht aussergewöhnliche Resultate und hebt sich klar von den anderen Mitarbeitenden ab. Verkörpert die IBM-Werte vorbildlich. Bringt mehr als in dieser Position erwartet werden kann. Hebt sich von der Mehrzahl der anderen Mitarbeitenden ab.

Findet Wege, um seine Stelle auszubauen und seinen Einfluss zu verstärken. Erfüllt die Erwartungen an seine Position. Ist vertrauenswürdig, zeigt ein angemessenes Mass an Wissen, Fertigkeiten, Effektivität und Initiative.

Im Vergleich zu anderen nimmt er seine Verantwortlichkeiten nicht voll wahr oder bringt weniger Resultate bzw. Zwei aufeinander folgende Beurteilungen mit einem Rating 3 sind nicht annehmbar in der hohen Leistungskultur von IBM und verlangen nach einer Verbesserung innerhalb einer dreimonatigen Periode, nach der ein neues Rating festgelegt wird. Bringt nicht das notwendige Wissen und die erforderlichen Fertigkeiten mit oder kann diese nicht anwenden bzw.

Sofortige, signifikante und anhaltende Besserung muss innerhalb von 90 Tagen sichtbar werden, ansonsten sind eine Versetzung oder Entlassung angezeigt. Rank moves up Probable or Outcomes remains flat Rank moves up Rank moves down or down or or remains flat remains flat Impact on Rank 2. Rotor position sensors, such as shaft encoders and Hall effect sensors, have been applied in SRM drives to determine the rotor position. These sensors increase drive cost and decrease reliability of the SRM drive in industrial applications.

In this study, incremental inductance of each active phase is estimated by using the terminal measurement of this phase. Simulation and experimental results have shown that errors between estimated position and actual position measurement position are less than one mechanical degree [ 7 ].

Husain and Hossain have presented the modeling, simulation and control aspects of four-quadrant SRM drives. A complex model has been described for the physical motor simulation to incorporate the important dynamics of the SRM. The results obtained from the final simulation model are extremely close to the experiments.

Such a model could be reliably used for performance evaluations and future developments [ 8 ]. A sensor-less drive that decreases cost and increases reliability, which extracts rotor position information indirectly from electric signals of motor terminals, is highly desirable.

A sliding mode observer, with its advantages of inherent robustness parameters of uncertainly, computational simplicity, and high stability, provides a powerful approach to implement sensor-less schemes.

In addition, a fuzzy logic current compensator FLCC for reducing torque ripple is presented. Simulation results have shown that proposed SRM drive decreases estimation errors and torque ripple, effectively. The method has good performance in a wide speed range [ 9 , 10 ]. One of the main problems in sensor-less SRM drive is position sensing at startup.

In this method, without using magnetic specifications, position estimation at startup can be done by a microcontroller. The magnetic flux in the SRM passes across the air gap in an approximate radial direction generating radial forces on the stator and rotor, resulting in magnetic noise and vibrations. Unbalanced radial forces acting on a rotor shaft are undesirable as they cause motor vibrations.

In this method, the SRM drive can generate simultaneously the modified shaft radial force and rated torque. The experimental results verified that the SRM produced smoother torque ripples when the proposed commutation scheme was used [ 12 ]. Harmonics magnitude of the radial force has basically role in acoustic noise generation.

They compared experiment results with finite-element method. Also, for acoustic noise reduction, Hyong-Yeol et al.

In this approach, laminations of rotor and stator have similar skew angel and radial force can be decreased significantly by changing air gap uniform. As we know, because of SRM dynamical nonlinearities, its control is complex. Hajatipour and Farrokhi have introduced an adaptive intelligent control based on the Lyapunov stability theory to control the speed of SR motors with good accuracies and performances.

The proposed controller is composed of a speed controller and a torque controller. Moreover, the torque ripple reduction was achieved by employing a neural network for torque estimation. The simulation results showed a good performance of the proposed controller in speed controlling and torque ripple reduction [ 15 ].

Most observers are static and once their gains have been determined. As we know, most observers are based model and uncertain in parameters will increase estimation errors. Therefore, online gain regulation can improve deficiency of based model observers. A new generation of observers named dynamic observers DO described by Divandari et al.

Dynamic observers can be used in tracking and drive systems. Also, main problem of SRM is high torque ripple, and this problem will be solved by optimization of motor design and improvement of drive performance.

We suppose a SRM is already built; therefore, torque ripple can be minimized by the optimization of the current waveform in the SRM drive. Simulation results have demonstrated that proposed drive estimates rotor speed with high accuracy for a wide speed range. Also, the fuzzy logic current compensator optimizes the torque ripple and decreases estimation error. In this chapter, to define a SRM drive, simulation, and investigations of the SRM behavior such as torque ripple, rotor position estimation, and acoustic noise, we present a nonlinear model of the SRM by using experimentally measured characteristics.

After modeling, with current profile optimization via fuzzy logic control FLC , the torque ripple is minimized. Acoustic noise sources in the SRM depend on torque ripple and radial force. Therefore, we discuss about radial force and important parameters in radial force and torque ripple, together.

Calculations of radial force with two methods are expressed as well. By using FLC, the torque ripple and radial force are optimized. Also, in this chapter, the study of a low-cost, sensor-less-based, and speed-controlled SRM drive system is considered.

The observer gains will be updated separately online at a wide speed range with band of estimated errors. Also, by means of indirect speed estimation, we propose torque compensation by current profile. In the following paragraphs, this chapter presents an intelligent solution for SRM drives. To study the characteristics of SRM drive system such as current profile, torque ripple, rotor estimation, radial force, and acoustic noise, we should present a nonlinear model of SRM.

Also, for SRM differential equations, it is required to study dynamic characteristics consisting of flux linkage and torque. The torque and flux depend on position and current. The flux and voltage for each phase of SRM can be expressed as:. The mathematical motion of the motor by the action of electromagnetic torque and load torque is.

As we know, the main disadvantage of SRM is a high level of torque ripple. One of the methods to decrease the torque ripples is the deformation of the current profile [ 17 ].

This chapter presents a new simple technique for minimizing the torque ripple via FLC. This technique is based on the transfusion of additional current in each phase by using FLCC [ 10 ]. Furthermore, torque ripple is one of the main acoustic noise sources in SRM drive. Therefore, torque ripple minimization techniques reduce mechanical vibrations on the bearing and increase estimation errors.

In this method, nonlinear torque curves are divided into seven sections 1—7. SRM torque in section 5 is nearly flat, but in other sections, negative torque slop will be compensated by using FLCC. Fuzzy rules are defined for a wide speed range near zero speeds up to rated speed. FLCC acts on the reference current when torque curve is 1—3 and 5—7 sections. According to current compensation, new reference current is.

As we know, fuzzy membership function arrangements and fuzzy rules determination have a main role in designing FLCC for torque ripple minimization. In this technique, the current and rotor position with seven membership functions as inputs and the compensated current with six membership functions as output are determined.

Also, at high speeds, fuzzy rules are moderated to PB because high current peaks in motor current will cause damage to power switches and the SRM. Input 1 current is defined 0—20 A, input 2 position 0—45 degree, and output compensated current 0—3 A [index 1].

Mechanical vibration and the acoustic noise in SRMs cause that they could not commercially yet competitive with other electrical machine drives. Noise sources recognition and elimination or reduction of noise in electrical machine drives can be dated since the s when new materials were presented to improve electrical machines designs [ 17 ].

When stator winding of each phase is energized by DC external supply, a magnetic flux will cross from air gap and an approximate radial force excites diverse mode shapes. Moreover, energizing stator winding in the SRM with a DC voltage generates lateral force, tangential force, and torque.

The current in the stator windings could generate a magnetic flux in stator winding that would emit acoustic noise [ 2 ]. Radial force is relevant to many parameters such as air gap length, position, rotor length, and rotor radius. Also, radial force depends on current square and turns number of stator windings, and sound power is relevant to radial force square. In the last decade, researches illustrate that most of the studies focus on the torque ripple minimization while stator and rotor shape design and radial magnetic force have principle rule at acoustic noise of the SRM.

In this procedure, torque ripple will be minimized and the maximum radial force decreases while we try to maintain maximum torque by the current profile deformation via FLC. This technique is based on transfusion of additional current by using FLC. As it was mentioned in previous sections, the radial force and acoustic noise are relevant to many SRM parameters. Magnetic specifications, machine design, and material type are effective in the radial force calculation.

For more study, we should first analyze the equations of radial, tangential, and lateral forces. The flux density in the SRM air gap between stator and rotor is given as. Also, the tangential force is obtained by dividing tangential torque by the radius of the rotor pole, yielding. The ratio of the radial force to the tangential force from Eqs. It is clear that radial force and torque are directly relevant to current square. The acoustic noise value is relevant to circumferential perversion due to the radial force wave.

The analytical presentation for the circumferential perversion can be defined as. R out , l stk outer radius and stack length of the stator m [ 2 ]. Depending on the threshold of human ear sensation, the reference of sound power level P ref is 10—12 W. Consequently, the acoustic noise power level in decibels becomes. In this procedure, we suppose that SRM has been already built. Our aim is to optimize radial force and torque ripple in the SRM drive so far as keep maximum torque using phase current waveform.

In the A area not only torque decreases but also radial force increases and even will be maximized, but in the B area, torque increases and will be maximized. Also, radial force will not be maximized. Therefore, with discussions of torque ripple minimization and fuzzy logic rules, we can keep the maximum torque and reduce radial force, and consequently, acoustic noise will be reduced.

Applying a shaft encoder in electrical machine drive decreases the reliability and increases the cost of drive. Therefore, usually researchers measure electrical signals consisting of voltages and currents for position estimation. The recent well-known methods for position estimation in the SRM drives have been focused on the three methods: Inductance-based estimation applying the two separated methods: Researchers have proposed many methods of sensor-less SRM drives in the last decade.

In these methods, estimation techniques have been applied for either rated speed or low speeds. One of these methods is sliding mode observer SMO. SMOs have many advantages such as inherent robustness in parameters uncertainly, fast computational, but they have some disadvantages such as sensitive to model changes and selecting of SMO gains.

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Researchers have proposed many methods of sensor-less SRM drives in the last decade. Erfüllt die Erwartungen an seine Position.

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Therefore, torque ripple minimization techniques reduce mechanical vibrations on the bearing and increase estimation errors.

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